In this episode of the ‘How and Whats of DORA’ webinar, host Kovid Batra brings back Mario Viktorov Mechoulam, newly promoted to Director of Engineering at ContentSquare, and Joan Díaz Capell, an Engineering Manager at New Relic. Following up on their previous discussion, they delve into the Monte Carlo method, exploring how it can bring predictability to software development projects. They discuss the importance of understanding engineering metrics, team dynamics, and project predictability, offering actionable tips on data collection and interpretation.
The panel also addresses questions from the audience, focusing on the practical applications and challenges of implementing these metrics in real-world scenarios.
🔁 How Monte Carlo Forecasting Drives Predictability
Discover how engineering teams use Monte Carlo simulations to predict delivery timelines and reduce uncertainty.
📐 Engineering Metrics That Matter
Learn how to track and interpret metrics like throughput and work item consistency for better planning and performance.
🔍 Reducing Team Friction with Better Data
Explore how transparent metrics help align teams, reduce confusion, and improve collaboration across functions.
📊 Avoiding the Pitfalls of Overestimation
Understand why overestimating team capacity hurts delivery — and how data can help you course-correct.
🎯 Using Metrics in Real-World Scenarios
Hear how leaders at New Relic and ContentSquare apply these metrics practically (and what challenges they face).
💬 Actionable Tips from Engineering Leaders
Get hands-on advice from Mario and Joan on integrating metrics into your team rituals like retros and planning.
Kovid Batra: Hi everyone. This is Kovid, back with another episode, another session of the How and Whats of DORA. Uh, last time we had set the stage with Mario and Joan talking about how to implement engineering metrics with some real practical examples that we brought in. We are gonna take that momentum further with them today. Uh, let me just quickly recap a little bit of the last session. So we, we ended our session talking about how to bring more predictability to the projects and Mario and Joan were really excited to talk about their approach, which is Monte Carlo. Uh, but we couldn’t cover a lot last time, so this time we thought like, dedicate a session to that and bring more predictability to your software development projects. Uh, I am thrilled to welcome again, Mario and Joan. Uh, Mario, congratulations. Last time when we met you were a senior engineering manager and you have been promoted, uh, to Director of Engineering at ContentSquare. So, many congratulations. And then we have Joan, uh, uh, who is engineering manager at New Relic. So, welcome both of you. Uh, I’ll give you a moment to say hi to the audience once again. Go ahead Mario.
Mario Viktorov Mechoulam: Hello. Hello everybody. I’m super happy to be here for the fourth time. I hope that the, the stories and experiences that we, uh, that we share today are, are to your liking. And feel free to drop comments, uh, to leave questions in the chat so that we can, uh, address them during this session or, or the next one.
Kovid Batra: Thank you Mario. Thank you. And thank you for mentioning to drop in the QnA because I usually forget to tell the audience. So, great. I mean, since the last two sessions we are together. You’re almost there. Taking up my position also.
Mario Viktorov Mechoulam: Promotion.
Kovid Batra: Great. Thank you. Thank you. Alright. Joan, over to you.
Joan Diaz Capell: Uh, no. Well thank you, uh, to having us again here Kovid, so we can continue with our conversation that we had last time. It’s my second time here and happy to be here again. Um, I think it is that the first time it was interesting, but we’re saying I hope that the second time it’s also keeping interesting. I hope.
Kovid Batra: Yeah, definitely, definitely. Uh, before we jump onto to, uh, Monte Carlo and talk more about how to bring, uh, predictability with these engineering metrics, uh, there was one thing, uh, that I wanted to share with the audience and with you guys. Uh, so I was talking, uh, to a few, few engineering managers on Reddit and, uh, I, I asked them like, what topics should we cover on this webinar? We are doing this kind of a webinar where we, uh, talk about practical applications of engineering metrics. So, uh, there was one good, interesting comment. I mean, a lot of people give their suggestions. I’ve added that to today’s episode, like today after Monte Carlo, if we are able to cover a few topics, I’ve added those. But one thing that uh, really striked out in those comments was one of the person mentioned that why to bring these engineering metrics? Why to bring that over micromanagement where we really don’t need anything? It’s pretty simple. Uh, sprints are there. Uh, we are able to look at a few metrics by ourselves. Then why, why to overemphasize on it? Why to really bring it on? And it’s not the first time I have encountered this, to be honest, but I really wanted to understand your perspective, both of your perspective on this. Like when you guys went out implementing certain things, was there similar kind of friction in the team, uh, from the management and how did you go about it and what’s your opinion about this thought?
Mario Viktorov Mechoulam: You wanna go first, Joan?
Joan Diaz Capell: Uh, well come, I can, I can be first. And I think one of the first things that you have to take into account is not being hijacked right, by, by the metrics. So like you are blindly focusing on improving a set of metrics because, because they are a trend right now. I think this is kind of the more complex, more complex thing. And if people, usually, from my point of view of my experience is when people are complaining about a set of things that you think, uh, is for the best of the team and are gonna help the team to be a, a, a better team; if people are complaining about that, is there’s this kind of friction is because people usually are against, uh, bringing change, not, not the change per, per se. I mean, if you are pushing a change and you, you are not explaining them why you do, do your thing or they are not part of this decision, for me is when there is this kind of, um, friction, um, because you’re trying to push for something that is nonsense for them because maybe you take the lead without taking into account their decision or their point of view. So I think this is kind of sometimes because we are focusing too much on metrics, um, and we’re not taking too much into account, um, why we are doing that, the team, uh, situation, if the metrics that we have already good enough why we’re adding more things, maybe we’re adding too much, too many things. So for me.
Yeah, usually this is kind of thing, the rational behind this kind of of complaints. Complaints or situation tensions because it’s different in different situations. Totally agree. Yeah. And I think what you mean is like, deep down you also believe that this is a good thing to do, but there is a balance that we need to strike.
It should be done in the right way. Uh, right. Things should be brought in and the team should be aligned on those particular things that we are measuring, that we are doing. And yes, I think that that’s a fundamental truth. That change is not comfortable for people. So until, unless you tie motivation and incentivization with those changes and what they could achieve as outcome, after that change comes in, uh, people would be more aligned.
I, I totally agree with that. I guess that we, we are already, I mean, you are changing all the time, but if people is pushing for the change, uh, then sometimes could be uncomfortable. Yeah, yeah. Yeah. Makes sense. It has to be a purpose. Indeed. Yeah. All right. Mario, what’s, what’s your pick? Yes, I, I like what John said.
I, I agree with it as well. Would like to expand a bit on something that we also covered in the, in the very first sessions that we covered together. Um, I, I think we have to get people to agree that some of these metrics can be very good indicators of perception, uh, from the point of view of the customer, right?
So we have, we have, we have to agree that, uh, and there’s, there’s different ways to do it. Uh, we went on, on detail during the first podcast, but once we do that, once we agree that, for example, customers interested in how quickly can we ship something to them, or whether they experience, uh, uh, incidents in production that, uh, increases their frustration, the next step to do is to ask the, the team that is, um, well, um, the team to which we are, uh, inviting to start using metrics.
Uh, what are the indicators that, uh, they think represent the work that they do and why, if, if they think otherwise, are these not metrics that they should be responsible for? Um, and uh, to me this is the, this is, um, the key to starting the conversation that they agree that cycle time, for example, is, is something that is directly perceived by the customer.
And we as the way we work, are responsible for longer or shorter cycle times. Like we’re responsible for quality and, and many other things. And then if we are responsible, it might be smart to measure it, it might be smart to, um, add some observation that, again, is within boundaries so that we have more information to make better decisions and to understand really, are we doing well as we think we are or are we not?
Yeah. Mm. Makes sense. I think, uh, what you just said brought in more clarity on what John mentioned. I think the purpose, the purpose has to be associated and that purpose has to be explained. The example which you gave about cycle time, let’s say for example, uh, should be known to the developers, to the team.
That why it is important for them to measure and how they’re accountable for it. So that totally makes sense. One thing that came to my mind, uh, when I was reading the, the comment, uh, so this guy is basically, I think a senior engineering manager or a director, uh, who, who said this and, uh, I felt somewhere is there a fear?
Uh, like engineering for business folks, uh, runs like a little bit of a black box because people are not aware of technicalities, the complexity, the code, how architecture works, what goes into it. So. Do you think, is this also a reason, uh, like the fear of making things transparent, like metrics would bring, uh, reality, uh, in front of the business execs or the leaders, and that that can create a little bit of a problem and people hesitate to bring it in?
Can, can that be also a reason? It could be, but it would very wrong. It would be very wrong if that is a reason, uh, which we used to shield ourselves with, right? I, I, I, I can understand the fear as soon as you start looking into, I think it was called the black box, right? There was a pause from John Cutler, right?
Uh, you, you see all these things and suddenly you, uh, you might understand that not everything that occurs is sorely the team’s responsibility. And, and by the way, when you, when you introduce this to the team, of course the team is directly responsible, but there are many things that happen in the organization that are indirectly responsible for this.
So teams don’t live in a vacuum as, as John likes to say, team is a, it’s a complex system and, uh, companies, organizations are a complex system of complex systems. And, uh, optimizing for a, for a local optimum is not, uh, it’s only gonna only gonna take you so far. So it’s important that people see this, even if it, it makes people feel uncomfortable or within and outside the team so that we can take action together because only the actions that are transverse, so for the whole organization can really have, uh, long lasting effects and, and larger impacts.
Makes sense. John? And do, do you have anything to say here? Uh, I, I agree with Mario. What, what, with what Mario was saying. Um, I don’t think, I mean, maybe is this the case that maybe people don’t want to show? And they, their, their data. It could be, but I don’t think that make any sense, to be honest, because, uh, it’s a complex process, all this software development.
I understand, but I mean, the more that I see, uh, or the more I dive into the data of each of the teams, as Mari was saying, I mean, at the end, we are all connected with different parts of the company, and engineering is one of the parts. There’s a lot of other things. So it’s complex. There’s a lot of data, but I don’t know.
There’s, there’s more things to improve also in different parts of the company. And showing your data is the best thing that you can do because you can, yeah, you can share how good you are in, in one part, and you can also share, uh, share how bad you are in one part so you can improve. Meaning that all the company can get advantage of that if you improve that.
I don’t know totally. It, it could be, but uh, from my point of view, transparency is key in this kind of situation. So showing my data for me, it. Is the best that I can do. Yeah. Let, let’s just say that, uh, it’s very easy to show great data and be transparent when the data is something that makes you feel proud.
It is, it’s way harder to do it when it, when it’s, it shows that it’s improve. It’s true. Alright. I think, uh, with that, I feel, uh, there needs to be a purpose, uh, to do things. And of course, if people remain transparent, we can bring that culture of having the right balance of metrics, empathy, understanding, and then actually move towards progress.
Having said that, uh, I think bringing an approach to bring more predictability, everyone wants like, forget for a while, uh, metrics, but every project manager, every engineering manager, uh, can relate to the situation where they are trying to give the timelines and almost every time. Failing to meet those deadlines, what they give.
So it’s, it’s a, it’s a repeated problem. Heard a lot. And today I think we have a new approach. Not exactly a new approach, but coming from people. Uh, we are gonna talk about it on this webinar, which is Monte Carlo. So Mario, I think, uh, would you like to take the lead and talk more about, give some introduction about this approach, how it helps engineering teams bring more predictability to their work?
Oh, yes. With pleasure. This is one of my favorite topics. Yeah. I think, I think you were just waiting for it. Yes, yes, yes, yes. Perfect. You go ahead. So what, what, what this, uh, method does is it tries to forecast the future based on the historical data. And the beauty of it is that everybody has, uh, has this available.
Uh, unlike other methodologies that perhaps require a specific investment of time, this is something that you have, uh, have already historical data to build up with. And if you think about it, it’s that simple. Doesn’t even require any mathematical complex, uh, uh, calculations. So let’s imagine that you, uh, you are tracking all the, um, all the throughputs, all the things that you deliver day by day or week by week.
And you can see that, uh, some days you ship one or two things, so other days you ship zero, some days you ship five, et cetera, et cetera. Um, so with, with this window of information, which we can say it’s, uh, I dunno, you can start with two weeks. Uh, a any data is better than no data. But let’s say you have two to four weeks of data.
And if you aggregate that, you can, you can see it in two ways. First, what, what, how much stuff you ship every day, or you can see, um, um, you, you can see how, um. Uh, the frequency in which we, you ship 1, 2, 3, 4, 5 items. And normally that will follow some time of long, uh, long tail distribution. Um, so yeah, imagine that.
Somebody asks you, okay, you have been shipping for two to four weeks. When are you gonna ship the next thing? And you have this information, what would everybody of us do? So you, you, you have a few choices. Um, you can sample randomly from your throughput from the previous weeks and it would not, uh, be super accurate.
But, uh, also it’s unlikely to be extremely wrong. Uh, you can also look at the long tail distribution and you can say, okay, so the highest frequency of items delivered in all these, uh, in all these weeks is one. So, I dunno, prob probability wise, you can say one and you’ll be more accurate, more likely to be accurate than to be wrong.
So this is how this works. Basically, uh, looking at the past, uh, and knowing how much work you have to do, you sample your previous throughput and uh, you sample large amount of times and you overlap all these times to get an idea of when can things be done. And the beauty of it is that, uh, not only you have the historical data is that it’s data based.
Uh, it costs absolutely zero and it’s better than other forms of forecasting because it doesn’t give you a single number. Doesn’t say, oh, by this date I will have it, or it’s gonna take two weeks, it’s gonna take three weeks. You actually see the whole spectrum of probabilities, right? Because when you overlap all these, all these multiple sampling, you see something like a bell shaped curve and you can see, oh, how likely am I going to ship this by the next week?
Very unlikely, maybe 10%, 20% based on the historical data. How likely am I going? Am I to ship this in two weeks from now or maybe it goes to 50%. So you can, you can take it, uh, all the, um, as further as you need to depending on the, uh, how, how much confidence do you want to have that, that you’re shipping by that date, right?
It’s all based on probabilities. Um, so this is how it works. So maybe I went a bit much in detail, but basically you, you sample historical data and you assume you try to predict, uh, with the same sampling. What, uh, how likely are you gonna delete x amount of number of tickets, but day of, uh, of work? And, and with that you can, uh, you can have a pretty accurate forecast.
So far, so good. Great. Yeah, so far, so good. But I, I have a few questions running in my mind. Uh, alright. Uh, do you want to add something or shall I ask my question? No, no, shoot. COVID. Okay. So, uh, Mario, you mentioned about first thing, uh, looking at the throughput of, let’s say last four weeks of data. So when you say throughput, I just want, uh, a more specific thing.
Uh, like, uh, should I look at, uh, issues that we closed or should we look at, uh, the deployment frequency, or should we look at, uh, maybe just prs getting much? What should I look at? Because what I feel, uh, when we need to give some estimations, sometimes as the engineer manager, uh, asking a team lead that, okay, can you do this piece of work in so and so time or not?
And then team lead is going back, checking what needs to be done and then giving an estimate. So what is that, uh, particular metric when we are analyzing last data in terms of throughput that we should be looking at? Okay. Very, very good question. Yeah. So if I had a team starting new with this, I would take all data, right?
So you, you can do this at multiple levels, your bottom level. So the, the more granular level is at the team level, and we are looking at tasks, bags, stories, uh, issues. You, you can call it whatever, but it’s quite granular. Uh, you can also take it a step further and, and look at features at epics. Uh, you can take it all the way further to strategic initiatives that take, uh, uh, quarters or years.
Uh, so let’s, let’s, let’s focus with the team that is starting with you. So get, get all the data that you can, because at the beginning, you, you actually want, want to have a bit more critical mass of data. So it helps to, to look at everything. When we talk about throughput, what we’re talking about is. Any piece of work that was completed, completed is something that the team should define.
Uh, but do note that, uh, the, the type of work, the nature of work a team does can be different. So not all the work is coding work. Not all coding work ends up in production. Well, a, b, s, um, but you do all the type of work, right? So you do bug fixing, you do support, you answer questions. Uh, you have some, uh, some conceptions to do.
Um, right? So there is a lot of work also that is not strictly coding work. And, and this forms part of the, the bunch of things, uh, of, of, of topics that a team spends time in. So why, I’m, why I’m saying take everything because. This is the reality today, or this has been the reality for, uh, for the last four weeks.
So if your team has dedicated 20% of their time, uh, to addressing, um, questions, um, support, uh, to the customers, if a, if a team has dedicated another 30% to the conception, to the discovery, to think about how they can, uh, tackle complex problems or how to take, uh, a part of the platform to the, um, to next gen.
Uh, all this is unlikely to change from timeframe to timeframe. It’s, uh, this is one, one of the first, um, issues that, uh, people who, um, try to adopt this, uh, this Monte Carlo forecast bring gap. So they say, oh, but. I cannot guarantee that my future work is gonna be the same as my password. And they’re Right.
But what doesn’t change, so maybe the specific work changes, but what doesn’t change is the nature of the work and the distribution of the work. Right. When does the nature of the distribution or the work or, or, or the type of work change, uh, when you grab one team and you lift them, you shift them to a completely new scope and domain.
Right? Right. But how frequently does this occur or when you completely disband the team and bring three people and then take out two people? This, this can also change the nature of the work or, or, or, or invalidate a bit the throughput that you have. But even if this happens once, it’s not gonna happen every, every month or every two weeks.
Right? Right. So after, after a while, you can say, my past distribution of probabilities is trustworthy. And even if it’s not 100% trustworthy, it’s better than anything else I have because anything else is not based on data. Yeah, makes sense. Makes sense. Uh, one more thing that, um, just came as a thought while you were explaining the bell curve and talking about the probability.
How is this model exactly, uh, eradicating the fact of recency? So just to give you an example, uh, in the last one week there were more of planning, documentation, things like that were happening. People were discussing, brainstorming on how it needs to be built. And then there was one week where we had only coding, right?
So when, when we are looking at data, we just need to make sure like all the aspects are covered, right? Mm-hmm. Because when I’m gonna predict something, I need to know that in the next, let’s say four weeks or six weeks, when I’m giving the timeline, is the same event gonna come into the picture or not?
Like if planning would come into the picture or not. And it would change how the timelines are being given. If next six weeks I’m just coding, coding, coding, then the scenario would be different in terms of delivery. So I just wanted to like clarify, uh, would four weeks of data be sufficient to predict something or, I mean, how this model would eradicate that recency bias?
Yes, you can experiment. You can experiment. I, I don’t think there is, uh, uh, one way to do it. Uh, but if I was starting with a new team, I would take as much data as I could initially. Um, okay. Then you, you could a, after a while, let’s say that you start, things start to change in a team. For example, you have periods where you have more plannings, more conceptions, more discoveries and, and periods that you have more, uh, more, more percentage of, of the time.
Uh, implementing you, you can choose to take a period which encompasses everything. Uh, as long as you think that future periods would be, would be similar. I, I think there is no reason to believe otherwise because a healthy team, I think it should be doing a bit of everything unless you have some, uh, dysfunctions inside.
Uh, that, that when you look at the, at a, the black box, right, and you’re trying to, uh, surface them, they, they will come up and then you have to, uh, maybe you have to take specific actions for that. But, but normally you’ll want to take a, a larger period, uh, that encompasses everything. But, um, but the sampling works anyway.
So you, you could take even a week if, uh, I don’t know, there’s teams that do, uh, sprints, uh, that take one week. Uh, that should be perfectly, uh, valuable. Now, um, before taking it a step further, because there is, uh, after you start with a team measuring this, there’s many other things that you can do. You can try.
There is, there is one more thing that is important, uh, and that is, um, we, we talk about work of different natures like planning, uh, conception, et cetera. But, um, when you start looking at this, they should not really matter, right? So, uh, unless all the conceptions that you do to put an example, take always above two weeks and all the implementation and development work, take around one or two days.
Um. If that is your case, perhaps you want to look at that first because it is strange, uh, that, that, uh, we are in a situation where coding work is sliced in a way and pushed, uh, in a way that it flows through the system very fast and conception work. Again, just to put an example, but it could be support, it could be any other thing.
It, it does not flow through the system or it’s not sliced in a way that allows it to move faster. So this is the first thing I would look like. Why? Again, just to have more, um, higher resolution because I have more data, uh, to use Monte Cargo. Then after we are pa does this make sense so far? Yeah, yeah.
Totally, totally makes sense. Okay, John, I see like, you want to, you want to mention? No, I think, no, no, I can just expand. What on what you were saying, Mario? Um. I think it’s a very valid question. What COVID said, and as Mario was saying, is kind of, uh, if you are starting with that, maybe you need a small sample of data that’s, that’s fine to, to start.
The only thing that, uh, it matters here is maybe it’s the accuracy. The accuracy. Maybe it’s not, uh, very high, but maybe it’s good enough for now as a beginning, you don’t have a lot of data, but you are starting. So you are, you have to enjoy the process as well. You don’t have to suffer it because if you start collecting a lot of data, you may be get a bit overwhelmed of all this data and at the beginning collecting just four weeks, uh, of your data a month, uh, two months, something like that.
You can already have a lot of, of data. You can already have, uh, triggered a lot of discussions and. Maria was saying this kind of, uh, probabilistic, uh, forecast, you might start seeing things like, oh, it seems that we’re taking longer than before. How is it that’s, that’s interesting. But what you said, COVID also the part of the, you’re at the beginning, maybe you are not sure about the nature of your, of your work.
Yeah. But as a, as a, as a thing that we, when, when Mari we were working together, I remember that we were also taking into account summer because there’s a lot of holidays there. So we usually, it’s like, um, don’t take the, the sample on on summer for this prediction because there’s not summer here. But maybe when we were clo close, um, um, close to to August and all this stuff that more people were on holidays.
We were like, okay, take into account that, that we will. Get, uh, uh, less, less throughput than, than, than before. True. And even we, we went even farther with that because we were, uh, we have kind of a different, uh, stories, uh, or tickets or items related with, with, um, product and other ones more on the technical roadmap.
So at some point we weren’t just, um, using one forecast for one and another forecast for the other one because we know that the capacity was different. So little by little we were, as I said, improving the, the methodology being more, um, comfortable with the nature of our work. So we increased the accuracy, but that’s the only thing that changed, that we increased the accuracy when we were knowing more and more of our, uh, processes.
Makes sense. Makes sense. Totally. I think that’s, that’s pretty good. A any other specific tip like this? Like, uh, any data that we should consider? Any data point we should not consider, uh, while evaluating, uh, for, let’s say timelines, any, anything like you just bring up the summer example. Anything that comes to your mind.
I mean, summer holidays, I mean, I said summer, but, uh, it will depends on different countries and culture, et cetera. But when you know that there’s a lot of, um, holidays or you know, that, um, you will decrease the, the throughput, take that into account because there’s something, as you said, maybe the planning moment.
But be careful with that because if the planning, it’s something that it’s happening maybe, uh, one week every month, just put it because it’s kind of an integrative thing that you have to take into account. Makes sense. Um, and I do, sorry. Yeah. No, I was gonna say that also depends. Also, you can also, uh, take that for, um, bags.
Maybe, maybe you can take bags out or because maybe they have another process, uh, the stories related to product. Maybe in one part, maybe the more expedite things coming from very urgent. I think this is kind of a nice tips to have into account when you are doing that, that stuff, when you want to improve it or iterate it makes sense.
Yeah. Um, yeah. Mario, please go ahead. Yeah, I think this is where it gets, uh, it gets more complicated. So I think the basics is some, something everybody can start with and it should already give you enough information, but there, there are ways to tune it, as John was saying. So one of them is being mindful of the period in, in my experience, and I think this is, this is applicable to most teams, um, periods of where all the team is off.
Like, uh, I don’t know, Christmas season, uh, of over a week, they do matter and we should include them periods of less than a week or where all, uh, not all the team itself normally, uh, don’t end up matter that much. So for example, I would, um, when when you have a a, a team that’s composed of five people, for example, and you have somebody that’s going a on a, a paid time off for one month, then you definitely want to, uh, take that into account.
If you have your whole team off for one week and you, your, your window for forecast is, for example, one month, you definitely want to time, uh, take that off. Uh, if you have people on and off, different people on the team during, during this month normally does not, uh, it’s not that relevant. Um, if you have people permanently leaving or joining the team, that’s also where you might want to adjust.
So most of the tools, they have a scaling factor that can account for this. So if your team. Is 100, uh, six, five people. Uh, you get a new joiner, perhaps you want to increase that from 100% to a bit more, so you don’t want to add 20% more because this new person will take a, uh, a few days, maybe a week, uh, to be, uh, fully operational and to ramp up in the scope.
Uh, so you, you can play with that value. The second thing that we can do, and that is, uh, what John was mentioning, it’s um, it’s that. Pure throughput only tells you amount of work delivered. But we are not telling anybody what, what is this work, right? So, um, and, and we use, by the way, use the forecast for two things.
The first thing, uh, is uh, actually I start with the first thing I started with was forecasting of when is work gonna be delivered. Uh, but, uh, what John was explaining right now and what I was mentioning about, uh, team, uh, people joining the team, leaving the team, uh, holiday season that is normally around capacity, right?
John was mentioning throughput. So if you want to think, uh, to, to, to have an idea of how much work and this still pool, uh, this team pool in, um, in a month or in a quarter, you can use this. And it gives you an idea, again, of raw throughput, of raw work done. And the same, same thing happens when you, uh, when you try to forecast, you have raw, uh, amount of work that you could, could do, uh, and you have an idea of when is that work, uh, likely to be done.
But here is where it gets interesting and what you were mentioning at the beginning, COVID, um, the type of work that you do, a team that is mostly focused on the roadmap. They have very little bags, almost no questions, no needs for support from the customers. Uh, the conceptions and the, uh, and the, yeah, all this work happens regularly and it just one small part of their, uh, of, uh, the throughput for that team.
Um, you will have a forecast that tells one story for a team that is, uh, buried into support, into incidents, into a lot of questions, into members of the team shifting. You’ll have a completely different, uh, story from the same forecast. So here is where you can start, uh, removing and adding filtering in and out, different work types.
One of the basic things that I, I like to do is when we talk with stakeholders about projects delivered, um, uh, we look at all the throughput, right? And the first thing, uh, uh, we do with John is we remove all the throughput that is back. That is support, that is, um, production incidents. So anything that does not equal to work delivered towards the roadmap.
Why? Because it’s not fair. We cannot say, oh, we’re, we’re pushing, uh, 20, uh, work items per, per week. But it turns out that only five of those were roadmap. All the others are all the staff, right? So when you do this forecast, uh, they look bad. So absolutely every team with which I’ve done this, you look at that the first day, and I think we told this story the other time.
Uh, you say, what, three months to do this? That’s, that’s not possible. I don’t trust this tool. But this tool is just telling, being honest with you. Just looking at your, your, your past and telling you, unless you have a magic wand and you’ve done drastic changes in the team, chances are that you’re gonna face exactly the same, uh, same thing in the following months, and you’re gonna be frustrated and you’re frustrating other people in, in the way.
True, true. Makes sense. So you can take that as, as far and as deep as you want to, but normally, uh, I recommend don’t go too deep, right? Because then you start playing a Yeah, that would be a numbers game. And maybe for an accuracy of five, 10%, you end up spending more time. And, uh, you’re, you’re not guaranteed to get this, uh, this accuracy either.
The, the real benefit here is that this is not no longer opinionated or based on gut feeling or pressure. This is. Based on data, purely based on data. Data doesn’t lie. And if you don’t like the data, okay, do something about the data. So the new data comes better. Uh, but again, this is continuous improvement in the team.
Don’t fake the data, uh, like you, we do with, uh, for example, estimations. Uh, it takes zero, zero effort, right? So one of the things that regularly happen when you do all the types of forecasts, like estimation, I mean always comparing to that because it’s fairly common in teams using, uh, most of the current methodologies is that you asked for an estimation while you are having the idea.
You asked for an estimation. When you start preparing the project, you ask for an estimation. When you found out all the dependencies, you ask for an estimation. Once you’re one week into the project, you’re asking estimation the first time you missed the deadline, right? So, uh, you have to take, okay, how, how much do they do?
How much left? But is there any new? So how many times does the team sit together? Right, because you, you are five points. Yeah. Three weeks from now, they’re no longer valid. They’re no longer five points or whatever. Right? They were never five points to begin with. But that’s a different story, right? So all this costs time.
So every time the team meets to do this, um, it costs money to the company and it brings zero value. The customer doesn’t care about this. So you can say, oh, no, we, we actually spent, uh, three time, three hours, uh, this week because we, we sat together to do estimations and the customer’s like, why you tell me this?
This does not add value to me. I don’t care. You can do whatever you want to do. You, I’m not paying for that. Yeah. Right? So, let’s do something that is zero effort, zero cost. So we have database, zero cost, and the final one is it’s much richer information. Your estimation of a single date, whew. Chances are you’re gonna miss that.
Look at how many five point tickets, uh, took in cycle time. And you’ll be surprised, you’ll see a, a, a probability distribution that doesn’t look, uh, equal at all. Yes. So you are giving to the customers and to the stakeholder much more transparency so that the people building roadmaps at the higher level, at the company level can really understand what are the trade-offs and what are the risks of having multiple teams working towards common, uh, objectives based on all this spectrum of information and this you don’t get with anything else.
I think this makes it much more convincing for me, at least at this point, that things would be more predictable. Uh, things would be more in shape. Of course, we can’t be a hundred percent correct, but this would be one of the most efficient ways to look at predicting, estimating your projects. One thing that I want to, uh, dive deeper and, uh, ask you so that our audience really take away some actionable tips also from here.
Um. How to collect those throughput metrics. Like if we are talking about issues, if you’re talking about, um, let’s say deployments, anything and everything that you end up collecting as data that you can think of, what would be the sources, how to collect them? Just, uh, a little bit on that part so that they find it easy.
Uh, on, on the execution front as well,
uh, by sources, again, that any, any whatever. Actually I started, uh, as a, as a, as an example, I started with collecting PostIts on the wall long time ago. Right? Okay. When I said this kind of stuff, I’m getting all, um, but, uh, for me the first thing is to collect the things that you’ve done, whatever tool that you’re using.
Okay. Um, and also, uh, if you want to increase. Accuracy, as we were saying, maybe you can start having kind of a categorization for it. Different types, types of work, because then you can, uh, understand that and also the date when you are finishing the things because you know the date. Also, you can put this seasonality on the, on the stuff that you’re finishing as well.
With this, I mean, you have more than enough to start to be honest with these three things, and most of the pointers that you’re talking about can be like pulled in from tools like Jira. Indeed. Okay. That could be, that could be. I mean, and, and it’s kind of easy to take it from, from them. Any, uh, app like that that collects the inform, the kind of information you can, you can take it for from it.
Sure. Yeah. You have that in typo, right? Uh, yeah. We do like, we, we actually go beyond Jira. We collect data from the CI CD pipelines. We collect data from your incidents. Uh, so yeah, gate, JIRA, CICD, all that collected. So I was just trying to understand like if there are multiple sources and people are pulling data from different sources, that’s where I think Typo could also be, uh, help helping them to collect that data and use that data to model for Monte column.
So that’s, that’s also one interesting point that you said that this, all this data that you can take, for me, it’s important in a second iteration, uh, of, of the Monte Carlo, because that’s a topic that maybe we won’t have time to talk because it’s kind of used as well, it’s the right size, but we, this is the elephant in the room.
We haven’t talked about the size of a Oh yeah. Of a, of a, um, item let’s say. But, um, if you have this different, different data, you, I mean, one of the parts that I like of this kind of forecasting is that you create a model and to be this model accurate, you also have to be in a good shape. Meaning that your system is predictable as well.
So all this, that data that you were saying, it’s good to know in a, in good shape you are, are you in a good shape or not? I mean, you are. They, you are CIC CDs for a reason. They are, uh, are your bottleneck. They are for whatever you are struggling with, uh, some BTS or something like that, or the prs are not working.
So with all these points, you can understand also if your system is not good shape, the best shape they have, the more accurate it will be your, your forecast. Makes sense? Makes sense. Yeah. And I think we, we really need to touch this, this point, John. Uh, we do it quickly because that’s, that’s the second biggest, uh, issue that people bring up when we, uh, yeah, when we, we in our experience, we have, uh, we have invited people to use Multicar and that is, oh, but then all tickets have to be the same size or all work items collect have to be the same.
That that’s not true. That’s not true. Uh, John has a amazing example, uh, for that. Uh, you can tell your, your, uh, your track story, John, but I, in essence, um, I, if today you have some pieces of work that are bigger, uh, other that are medium sized or that smaller and just making these, uh, sizing, uh, up from my head again, the nature of your work is unlikely to change drastically.
So chances are in the future you’re gonna have similar sized work, but even more you can have, uh, some pieces of work taking hours to a few days. So one day, and you can take, have others taking one to two weeks. In the end, all this is very likely to even out, right? So you have your bell curve and you have the extremes.
You should not worry that much about the extremes. So the extremes can even out. And, and with all the work that you’re doing about continuous improvement, about uh, um, things that, even if. I’m saying do not focus on having all the work, uh, having the same size. The thing is that we do in continuous improvement on, on focusing on cycle time, on slicing, on having single acceptance criteria of having more pairing and teaming on, on work, on eliminating bottlenecks, wait times, et cetera.
That narrows your bell curve. Therefore, it leads to having, uh, more accuracy and indirectly to things, uh, to work that is, uh, more equally high or at least, uh, more bounded. But even if you have some extreme, you should not be, uh, worrying about them because their probability is low. Uh, it won’t change. The nature of the world won’t change in future iterations, and they can even out with others that are smaller in size.
Makes sense. Alright. I think, uh, with this, uh, I think we have got a lot more clarity. Uh, one last thing, just I, I wanted to, uh, understand. Like how better your approach to predicting has become. I mean, not approach, uh, how better your results are. Now when you predict, uh, projects in your teams, like earlier maybe you were, uh, following some, uh, basic approach and when you brought this into picture, how much improvement did you see?
And I, I’m asking this question just so that the audience also understands that what’s the impact of putting in effort on this kind of an, uh, interesting model? I, I would say very accurate. Very accurate. Um, I have to confess that right now I’m not using it, but only because we have done all the other work around cycle time that already leads to that result.
So we are in a point perhaps where stakeholders are not really that interested of when are you gonna deliver something? They know that if they prioritize the right way, work is coming up, work is coming out. So they, they already, uh, have what they need to do. I know other teams that have adopted it is they, they’ve shared feedback, uh, last few weeks.
Uh, accuracy is, is, um, scary. Scary, right. Um, okay. And, um, and yeah, I, I think the way the days, so the, the seasons that I’ve used it the most, it has been to prevent risk or to prevent missing key, uh, deadlines. Yeah. And for those, it was very easy to see way ahead of time that we were not gonna hit the target.
So last year, uh, we had some very challenging projects with a lot of work in, in, in one of these recent teams that I work with. And, um, so there was a lot of word that was identified, word that was identified and already, um, let’s, let’s say, uh, more, more granular. So more actionable. And there was word that was not identified.
So we had an idea, rough idea, but it was very, very, very far away to start working and, and caring about that. So as soon as we realized that, hey, looking at the current pace, at the current throughput and imagining that we have a similar amount of work ahead of us, we’re not gonna hit the target. So that helped other teams jump in, uh, a way of, uh, outsourcing internally and hit the deadline in the end.
But, uh, what we are trying to avoid with this method is also, uh, what we see many times, right? So everything is going all right, everything is gonna right. No risk, no risk. We’re gonna hit the date here, the date, and then last week before that, oh, no panic, everything is red, we’re not gonna hit it. Right? So people are naturally, uh, hesitant, uh, reluctant to like to, to show the bad metrics, right?
Uh, and they normally try to say, no, everything is going all right, but in the end it’s not. Um, these type of models can help us see out ahead of time and take action to prevent it. Perfect. Perfect. Guys. Uh, we have only 15 minutes left. Uh, we have q and a to, uh, go ahead with, there are a few questions that I already see.
Uh. I think we can, we like go to the q and a now and maybe take the next session for the topics, uh, related to how map engineering metrics to business KPIs and how to improve processes with data. We’ll, we’ll cover those topics in the next session. I am hoping you both are not running away. Ah, uh, we, we’ll, we’ll be, uh, coming again in the next month also, uh, talking about these.
Uh, so let’s go back to the q and a, uh, because we have some questions here. Are we good? Yes. Okay. Perfect guys. Thank you. So we’ll take a minute break and uh, then we’ll start with the q and a.
Uh, so we’ll start with the q and a. Uh, first question is from Aloni. Uh, so that’s for you John. Uh, how do you make sure metrics aren’t just numbers for leadership, but actually help engineers improve their workflow? Uh, yeah, that’s a good one. I think that this is related to what we were saying, um, before.
Um, metrics should be not, they are not the end. They’re kind of the mean, right? How we are moving to an improvement. So from my point of view is, um, they have to, they have to know. I mean, you have, you have to have this conversation with the team, uh, regarding what they would like to improve first. So then connect their motivations, they purpose.
Uh, we were saying with that metrics, if there’s no disconnection, uh, they will be numbers as we said. So numbers without any kind of, uh, feeling or meaning for them. So you have to create this connection, otherwise complicated.
All right. Uh, uh, Mario, you wanna add something here? Uh, no. No. I think that’s, that’s the core metrics should feel, should help the team feel proud. Great. Thank you John? Uh, I think I just wanted to add one more thing here. Like when, but I have seen while talking to our customers also. The leadership who is motivated towards looking at engineering metrics and numbers, they are usually motivated because they actually see how the business goals and these metrics can align.
So I’m just adding to your point, John here, that if the teams know that, uh, this is the metric which defines what work they’re impacting, which metric they’re impacting, and if that can be tied to the business goals. And let’s say EMS or let’s say the director of engineering are actually going and explaining those things, that X team improved Y metric.
And that ties to the business, KPI of let’s say increasing revenue or, uh, reducing cost, right? So automatically things form, uh, uh, great binding there and intrinsic motivation drives into the overall culture. So that’s what I have seen Jo. So no need to answer your question. I think what John said, I just added to it, uh, from my example.
I agree. I agree. Hundred percent. I mean, at the end is what we are saying, I think some purpose and some feelings there in that Yes. In the numbers. Totally. I think, uh, we can, we can move on to the next question then. Uh,
this is from Nisha. Uh, what’s one metric outside of Dora that you personally swear by for understanding team performance And, uh, she hasn’t mentioned the name, so, uh, Mario, maybe you can take this one. Um, I have a couple, but, uh, I’m gonna say one for another question. Um, I, I think one that we can, um, rely on is to see what is the percentage of time that work spends blocked or waiting.
So if we track that, we can quickly understand from what we are handing out to the customer, how much of the, of the time to wait for it is a waste, and that we should address. Remember, a customer doesn’t care if there is a dependency and for us it’s very important they don’t pay for that. So we should eliminate all these things.
Perfect. I think, um, John, any, any thoughts? I was gonna say, uh, age of the tasks that have been, uh, in progress. So this is also related more or less what Mario was saying. So understand how much time thinks that, that, that the are connected what we were saying before, the shape if you’re in a good shape or not.
For me it’s a good indicator. Makes sense. Alright, uh, moving on. Uh, this question is from Vishal.
What’s the most common mistake you’ve seen New engineering managers, uh, when they introduce metrics to their teams. Interesting. Yeah. Uh, that’s a good one. If you wanna can start? Yeah. What please. I guess, I guess it’s also related to what we were saying that, uh, introducing a metric because it’s trendy because everybody’s using, uh, let’s put this metrics because people say that they’re very useful.
Let’s follow this, this. This thing, this is tricky. Again, why? Because there’s not a why, uh, after that and there’s no purpose what you’re trying to improve. Um, for me, this is one reason. And the other one is adding too much or too many, sorry, too many metrics at the same time because it’s could be overwhelming, uh, increase the cognitive load, a lot of metrics to pay attention.
Somebody can say, Hey, this is too much for me. I will continue doing what I’m doing for me was successful. So I’m not, I don’t care of metrics. For me, this, this is the two things that I’m, that I’m seeing. Great. Yeah. Mario? Great examples. One more, uh, I can add is, uh, don’t do them yourself. This is also connected to what John said earlier.
Uh, you need to help the team see the value. Uh, if you do it just yourself, it’s gonna be just reinforcing, um, the misconception. It’s management metrics. And disconnect the team from them. Perfect. Um, then we have a question from, uh, when a Dora metric, like change failure rate starts to go up, uh, where do you typically start your investigation, team process, testing or something else?
So, uh, yeah, I think very relevant. Uh, these metrics are the means to understand if something is going wrong, uh, what’s underlying, uh, what’s the root cause, that is something which we yet need to identify. So, change failure rate, uh, your deployments are failing. So in that case, what would you go about searching for first?
Mario, you should, uh, okay. Okay. Um, so the. The, the, the cheapest, uh, moment. Um, so the, the moment at defect is the cheapest, is the, uh, the earliest we take it. Uh, we, we find it, right? So that can happen during, during the coding phase. Um, so if something has slipped through coding, through reviews, through qa, through UAT, through pipelines, through all the things that we have, and, uh, it, it has blown up in production, we might start in looking at any of these.
I think it makes more sense to look at the, uh, to start from the, uh, from the end of it. So to try to understand how these thing, uh, so what, uh, what was missing? What gate let, um, uh, yeah, these, these, uh, changes, uh, slip into production and, and start there. But of course, we should not stop there. So just because they stop sleeping doesn’t mean that they, uh, they’re not caught at the moment where they are more expensive to fix.
So we should follow this process all the way, all the way until the, uh, until the coding phase. And I would also take one step in the other direction. Uh, let’s see how many customers were impacted by this, uh, and how many customers discovered this because there’s only one thing that is even more expensive than, uh, uh, failure in production.
That is failure in production that this the customer discovers before you. Yeah, makes sense. Totally makes sense. I think. Perfect answer there. Uh, John, you want to add something? Uh, I think that was clear, Mario. I mean, now, so we said that this is kind of a complex, complex, uh, problem. So there’s not a complex, there’s not a simple answer for that.
But, uh, I agree with, uh, what Mario said. I mean, starting looking at that point and little by little, maybe we, one thing if you want something concrete besides what we mentioned is, uh, do double check that projects or, uh, features do have a directly responsible individual, some, somebody that is owning them or driving them start to end.
And if you don’t have this, this role, or if this is not part of how the team behaves with the work they do, um, this is another thing that can be, uh, can be, uh, worked on transversally.
Cool. Thank you. And, uh, one last question that we’ll take now. Uh. This is from Neth. Yeah. As an engineering leader, I want to know how is my team doing? What metrics should I look at? I think we have already covered this, uh, in a way, so I, I’ll jump onto the next one. Uh, this is from an unknown user anyway.
Uh, if a developer would like to understand where they are excelling and the areas for continued growth, what matrix should they focus on? Okay. Um, I think Mario, uh, we, we have kind of, uh, little bit covered it earlier also in the previous session, but, uh, if you want to take this, uh, sure. Um, so my advice to this developer is do not look at individual metrics.
So metrics for individual. Look at the metrics for teams and look at the metrics for teams that the team is struggling with and think about what are. What are the actions that I can take and how can foster the team to do better on those metrics? So that’s, um, so yeah, sorry, I, I, I focused on the growth part, but I see now that, uh, they are asking, um, they like to understand where they are excelling.
Um, so we can turn this the other way around. So the areas of the teams and the metrics of the team where they have contributed the most to see, uh, to these, to see this positive impact. Yeah. But again, I would advise against looking at individual metrics. Individual. Yeah. Yeah. Okay. John, do you have a different opinion or you want to add some thoughts?
Uh, no, I mean, uh, Kai, I’ll agree with what Mari was saying. I mean, that’s, uh, again, with metrics, there’s different metrics that you can take, but, uh, how you can help the, the team to improve, I think this is the, the best. So knowing which are the things that you can impact them, impact and improve. So they’re kind of related of things that he or she can, can continue growing and improving.
If there’s any other things, particular, particular things that they are not related with how the team is working. That could be tricky at some time because maybe you can start fighting for different goals than others. But yeah, however, I think, uh, I think this is, uh, my, my personal opinion about this thing.
If I’m part of the team, uh, I, I know that we are working towards improving, let’s say, our cycle time. Okay. How I have contributed to it, uh, would really matter to me. Like I would want to see how much contribution have I made. If, let’s say we are trying to improve the code review time, and I’m one of those senior developers who look at the, uh, uh, like PRS for review, I, I would definitely want to see how much I have contributed in, uh, improving that review time.
The PRS that came to me, did I complete them in those benchmark timeframes which were assigned for the team as goals. So don’t you think, uh, it can actually work in a very positive way for someone to have that clarity? Uh, I mean, I, I’m sorry to differ here, but, uh, what, what do you think I. I think it depends, right?
Um, because maybe you are trying to improve, I mean we are part of a whole, right? So if you are trying to improve the time, the prs and you improve that, but then uh, you are deploying more bags, what is the trade off, right? Maybe. I mean, I think that you should, we are part of a whole also. Um, the part that we are not having the prs, we have to improve how we are doing these reviews, but we are in a good shape on, on bugs.
Then what I’m trying to say is, uh, it’s, I’m okay with what you’re saying, but you have to define social guard rights, guard rights. Like if you are doing that thing, you cannot change these other ones. They have to stay how they are. So I can try to improve this, be more fast in the pr. So I’m doing my reviews, but they have to keep an eye on that.
Okay. They’re staying in the same, in the same situation than before. I’m improving now. We are having better, uh, faster pr. But we are having problems with the deployment. Hmm. Makes sense. Okay. Let’s, let’s have this one. Mm-hmm. Yeah. I think there is very few metrics that are, that actually matter and that are close to having a direct impact for the customer.
And those are all team metrics, right? Like how, how quickly can we ship working software in the customer’s hands? That is, so if, if you, if so, the thing is, if we, if that is not working well, that’s when you might want to say, let’s dive deeper. Let’s see what happens here and what happens here, what happens here?
And then let’s look transversely. Then maybe you see all review time is high, but, uh, I, I don’t think we should focus on review time, lowering review time, because that might not be your bottleneck. Uh, may maybe your review time compared to the time in progress and every, everything else just, it just makes sense, right?
And, and, and the cycle time in 20 is still, uh, very, very reasonable for the customer. So that. Lowering that further. Uh, I don’t know. It might make people feel better, but it has no actual impact for the customer. And, um, yeah, I had some, some other top point based on what John said, but it slipped my mind.
Okay, perfect. Great. Uh, thank you so much, both of you, uh, for your time sharing your experience, talking about Monte Carlo. This was really, really an, uh, interesting deep dive into something that I always wanted to, and, uh, I’m happy that we did it together. Uh, there are a few topics that I had, uh, aligned for the session, but, uh, we’ll take it in the next one.
And, uh, thank you once again for taking our time today and sharing your thoughts. Thank you, Johan. Thank you, Mario. Thank you. Thank you. We, we didn’t even go into the. Pitfall of Monte car, so that, that is a whole topic on its own. I have I a note of that. We’ll, we’ll definitely cover that in the next session.
Yeah. Thank you. K. Thank you, Joan. Thank you. Thank you. Thank you.
In this session of ‘The Hows and Whats of DORA’ Webinar, powered by Typo and hosted by Kovid Batra, panelists Mario Viktorov Mechoulam, Senior Engineering Manager at ContentSquare, and Joan Díaz Capell, Engineering Manager at New Relic, share their expertise on DORA metrics and their real-world application to enhance engineering outcomes.
They discuss how to effectively use DORA metrics to track and improve dev team performance, and explore advanced topics like Monte Carlo forecasting and using qualitative data to track team health. The session emphasizes the importance of aligning engineering metrics with business goals and product success, avoiding vanity metrics, and leveraging data to improve collaboration and team well-being.
The panel also responds to audience questions, sharing insights on overcoming resistance to metrics adoption, managing cross-team dependencies, limiting work in progress, and balancing speed with stability.
🚀 How to Use DORA Metrics Effectively: Unlock actionable insights to improve engineering performance and delivery speed.
🔍 Monte Carlo Forecasting: Predict delivery timelines and reduce uncertainty by applying data-driven forecasting techniques.
📊 Aligning Metrics with Business Goals: Understand how engineering metrics correlate with product success and business impact.
⚡ Avoiding Vanity Metrics: Focus on metrics that drive real impact rather than those that just look good on dashboards.
🏆 Leveraging Qualitative Data for Team Health: Use qualitative insights to improve collaboration, well-being, and team culture.
💡 Managing WIP and Cross-Team Dependencies: Practical tips on limiting work in progress for better flow and reducing bottlenecks.
Kovid Batra: Hi, everyone. Thanks for joining in for the DORA Metrics webinar ‘The Hows and Whats of DORA’, powered by Typo. This is Kovid, your host, and with me today, we have two amazing speakers who are some of the front runners of bringing data-driven engineering to the engineering world, making dev teams even more impactful. Please welcome the metrics expert tonight, Mario and Joan. Thank you, Mario. Thank you, Joan, for joining in. Uh, so guys, both of them, uh, come with real and deep experience of educating and implementing engineering intelligence in various dev teams. Uh, Mario, who has had like 15 plus years of experience, uh, he, he is coming to this webinar for the second time and we will see him more frequently coming in. Uh, he, he’s a Senior EM at Contentsquare. Uh, hi Mario.
Mario Viktorov Mechoulam: Hi, Kovid. Hi, Joan. Very happy to be in the live panel again. Thank you for having me.
Kovid Batra: Pleasure. Pleasure. Uh, then we have Joan, uh, who, who comes with, again, 18 plus years of experience in Agile coaching, and he’s an EM at New Relic. So he is managing dev teams at a great company. Uh, I, I, I can assure the audience that the door’s fully packed with insights to share with us today and help us navigate the landscape of engineering metrics. And, uh, this time, uh, this session is really special. Uh, why it is special, because today the insights that, uh, the audience is gonna learn, uh, you guys are gonna, uh, speak about are coming directly from the engineering leaders who have been associated with Typo, who have encountered so many use cases that they’ve come to Typo for understanding how to leverage those engineering metrics and tooling like Typo to actually bring that data driven approach and drive efficiency. So it is gonna be a real, real insight from the real world. Uh, but before we dive into these, uh, practical applications of DORA and engineering metrics, uh, I wanted to do some ice breaking with, uh, Mario and Joan. Uh, so basically, uh, we, we start with, uh, usually we start with some different kind of questions about your personal life, about your hobbies and things. So I, I have come up with something. Um, I would say, uh, you, you would like to talk about, and that way we would also, uh, get to learn more about you. If you don’t like the question, you don’t have any option, you have to answer it. Alright. Uh, so are, are you guys ready?
Joan Díaz Capell: Ready.
Mario Viktorov Mechoulam: Let’s do it.
Kovid Batra: Alright. Alright. So I think, uh, we can, we can start with Joan. Uh, Mario, I’ll give you the second seat because you are here for the second time. Let the audience know, uh, Joan more now.
Joan Díaz Capell: I like that.
Kovid Batra: Okay. Okay. Uh, uh, so I think, uh, Joan, the first thing that we do, uh, to start with is, um, we usually want to know, uh, what are your hobbies? But this time I have thought about something which usually everyone wants to do in this world, but I don’t know if people really want to talk about it. Uh, it’s more about the financial freedom. So everyone is working, making money, and of course that’s, that’s something that we need to live on, but everyone needs that financial freedom and that has been a term quite, uh, like heard quite often a lot. Uh, so what do you think, what is your stage of financial freedom? And let’s say you achieve it. Uh, what would you do at that point and why? Of course.
Joan Díaz Capell: I’m not at that point to be honest, but, um, if I, if I was able to get to that point, what I would like to do.. Um, first I think that, I mean, I would, I would spend most of my time on, on the hobbies that I like, right? So I really like doing sports. So I will be very focused on, on, on sports. But on the other side, I also like this, um, uh, also, I like reading also books about philosophy, something like that. So that’s kind of a part of my side, that side that if I have all this freedom, maybe I will just take my time to just observe, rest, relax, and, and enjoying the, uh, doing, I’m not gonna say doing nothing, because I will just meditating and thinking, uh, more introspective. So I think I will try to just chill, slow down, uh, enjoy my, my, my, my hobbies that, I mean, usually is everything related with the sport to be, to be honest. Like surfing, skiing and going to the gym, et cetera. And apart from that, reading and just, and relax and chill, I think that I will enjoy that.
Kovid Batra: That’s great. Uh, but I won’t stop bugging you here. I would love to know like, why would you want to do surfing? Why would you want to do skiing? What does it bring to you?
Joan Díaz Capell: Ah, wow. Uh, I think it’s this kind of challenge, this kind of mastering the, the, the technique, the, the environment, trying to fight in order to improve and be better with your, yourself actually. So trying to be your, your, your best day after day. And it kinda is nice some of this sports because sometimes it’s like, wow, I’m think I’m doing very, very, very bad this, this time. But then it’s, oh, I do it very, very good. So I think that you also have to live with this situation that there’s good days, you have good days because bad days exist as well. So you have to also to, to, to find that balance.
Kovid Batra: That’s interesting. Thank you. Thank you so much. Uh, Mario, the same question goes for you. Yeah.
Mario Viktorov Mechoulam: Yeah, yeah. I think, uh. I think if as first to, to do that, I’m also far away. We have to, we have to leave Spain, Joan. I think financial freedom is not gonna happen. Uh, jokes aside, I think I would be, I would be writing. I, I love writing. People at work that have had the, have been unfortunate enough to, to suffer me and suffer my, my walls of text, they know I, I like writing. So since I was a kid, I like reading and, and writing stories. So I think I would dedicate, uh, a good chunk of time to that, hopefully in some, uh, on some Caribbean beach. And, uh, if, if I achieve financial freedom at a point in my career where I’ve, I have amassed enough, uh, experience also, I would love to share, I would love to help people, um, with some coaching, with some, um, yeah, I dunno, some, some experience and some lessons that I’ve learned so that, uh, it can help them as well.
Kovid Batra: Perfect. Uh, is there anything specific that writing brings to you? As I asked Joan also, like, why exactly would you want to do that? How does it help you? And what kind of writing specifically if you have to talk about?
Mario Viktorov Mechoulam: Uh, I would say between science fictions but also mystery. I think writing, well you can, you can write your own book, your own story and then, uh, being able to communicate to people, being able to let them experience something that unique, I think it, uh, it means a lot to me.
Kovid Batra: That’s really nice. Great, guys. I think both of you have something deep inside, uh, and that’s really encouraging, uh, and good hobbies to have for sure. Uh, alright. I think on that note, we can definitely get started and tell the audience what we have to offer here. So, uh, before we jump onto the main section where we start talking about the engineering metrics and the use cases, uh, the first thing, uh, I would like to understand from both of you, uh, you both have been like flag bearers of engineering metrics in your teams. Uh, we definitely talked about it, but why do you think it is important? And I mean, there is an obvious answer to it, probably to both of you, but there are a lot of people out there who are seeking that motivation, seeking that understanding of how it can actually impact their teams, their careers. So, uh, Joan again, uh, would you like to go first and tell us why is it important?
Joan Díaz Capell: Sure, sure. I mean, for me it’s, um, the sense of also, uh, of improvement as well. I mean, it seems that we want to be better sometimes and we want to be, uh, better, better at what, what we are doing because we are professionals, right? Um, how do you know that you’re, you’re, you’re working better every time and you’re improving? Sometimes for me, it’s like if I don’t have some kind of metrics, um, I have the feeling that I’m quite blind. Right. So, um. How can I put some light? How can I enlight also the, the progression or what, what the challenge that we are doing if I were, we’re doing in a better way or not? So this helps me also to understand a little bit our improvement also, and also can try to get also some information about the environment that is surrounding us. Like, uh, it’s kind of a complex problem, so we are dealing with different metrics, so they are correlated or not, uh, we are trying to push in the same direction or not. I mean, it gives me some, some light some way also to, to see if we’re improving or not. So it’s kind of, um, it gives me some peace, I would say.
Kovid Batra: Perfect. But, uh, don’t you think, Joan, almost all the engineering leaders, engineering managers out there have some way of measuring what teams are doing? Uh, why we are here today is to explicitly highlight the importance of bringing in a more sophisticated structure, uh, when we talk about engineering metrics. So what would you tell, uh, a person who is doing some level of measurement? Maybe they’re just looking at their sprint activities and understanding how many story points they completed, or they’re doing first level understanding of how their, uh, developers are, uh, completing the committed stories. But is that enough? Like, is that sophistication required in this structure? Uh, and how it is important if it is required?
Joan Díaz Capell: Uh, I would say from my point of view, that it’s never enough. I mean, I usually like to challenge the status quo. So I would say to those people, to, to these people, like, be curious. Try to understand if there’s more things or. Don’t trust, don’t blindly trust what you used to do before or what you hear that all the trends that people are doing. Try to understand also if what is your environment, if there’s any other things that you can incorporate, which are the things that you’re more curious to know about, uh, try to get more information about this because I’m pretty sure that there’s plenty of more metrics that you can use and you can get a lot of advantage of them.
Kovid Batra: Makes sense. Um, Mario, do you have anything to add here?
Mario Viktorov Mechoulam: Um, I think, no, I think Joan’s answer was very spot on. Uh, we also had, uh, a few months back, Clint here, who used to say, ‘I cannot fly blind.’ And that describes it pretty well. So to avoid repeating myself, I think, uh, I, I I like to quote Peter Drucker, like, ‘what gets measured gets managed.’ And in this case, I think if we want to do, to do better and understand what we are doing, and if we are improving, we need to, we need to measure.
Kovid Batra: Makes sense. Joan, back to you. Uh, anything specific, uh, any framework, any set of metrics that you would always recommend to engineering teams, teams who are getting started? Uh, any kind of tip on that part?
Joan Díaz Capell: Uh, framework, framework per se, I’m not a big fan of, uh, uh, and a box that you can just take it and, and use it because that, you know, there’s this quote, uh, never remember the name of that guy that said that, ‘for every complex problem, there’s, uh, an answer that is simple, clean, but wrong.’ Right? So, um, I would say, uh, it depends on, on, I mean, I usually start with a subset of things that I like to have to, to keep in mind, but then I can start adding more things like, uh, I mean the basic ones like throughput, uh, cycle time. Um, WIP, in order to understand how is the, the, the system at that, at that point with a very small set of things. And then I start adding things if I think that they make sense or not, or maybe I add one and then remove. But I don’t have any particular, um, framework to be, to be honest, uh, Kovid, I just start to start small.
Kovid Batra: Makes sense. No, it totally makes sense. I think, uh, every situation, every problem has its own, uh, nuances, which needs to be understood and based on that, one can actually apply something. It could be a part of some framework. It could not be. We need to decide on the basis of the situation and the problem that you’re dealing with. Totally makes sense. Mario, you have any, any opinion on that part?
Mario Viktorov Mechoulam: Uh, very similar. You can see how we used to work together. I learned a great deal from Joan. I think less is more when you start, but there is, there is a, I would say a small set of metrics that you cannot go wrong with. I mean, they have, they, they stood the past of time, so I, I love cycle time. I love WIP. Uh, I love throughput. Just with those, there is so much depth once you start digging that you can already understand, uh, a great deal. And after that, it depends on the context and it depends on what you’re pursuing. So if you are interested in some, uh, X event, X outcome, perhaps you can, uh, look to understand which are the metrics that would be, uh, key indicators, lagging indicators to that, and see, um, what is the team comfortable with measuring, uh, around that, that can give us information.
Kovid Batra: Makes sense. Alright, guys, I think, uh, that’s, that’s a decent start to, uh, handling engineering metrics and using them, leveraging them to bring more efficiency. Uh, today I have jotted down a few, uh, use cases as I, as I told you in the beginning, that there have been, uh, things that we have been discussing with our users in Typo who come with different, uh, scenarios where they want to measure certain things to improve, uh, XYZ in their engineering teams. So, uh, let’s see how many we can cover today. I, I have a lot, of course, we can take it to the next session also, but to start with, the most prominent case is, uh, that we are dealing with is understanding the impact of improvement. So that’s the first use case I, uh, wanted to discuss with you both and, uh, impact of improvements, I, I’ll detail out a scenario and, uh, maybe then you can get a better understanding of what, what I want to discuss and what the, our users wanted to understand. So, let’s say there is a senior engineering leader, uh, who wants to show the executives that the organization is more efficient at releasing software now than the, than organization was two years ago. How the, that efficiency, uh, has translated into lower cost, how that leader could actually use certain set of metrics, uh, to showcase that, that impact of improvements that have happened in the last two years. So what is your outlook on this situation? Which metrics should be used? Is what I wanted to discuss with you.
Joan Díaz Capell: Mario, shoot.
Mario Viktorov Mechoulam: Okay. Um, alright. I think what, when, when I hear this, we’re talking about yeah, efficiency and this is, this is a word that gets thrown around, uh, a lot lately. Um, but before we delve into that, I think we need to define what, what is efficiency, right? So is it, uh, is it, um, I dunno, shortening the cycle time? Is it, uh, reducing the number of, of bugs? Is it, uh, doing, uh, more, uh, more features with the same team that you have today? So we have to, we have to define that. And, and once we do, and we can go in detail into what for me is, are some indicators of efficiency and then hear from, from Joan as well. Uh, and after that we have to, we have to compare, right? We have to measure and we have to compare because if we’re talking about a two year period, um, I would like to think that we are not, we’re not in a situation where we have worked on a system to improve efficiency for two years and then bang, we release that and we try to measure and compare with what we had two years ago. So many things change in the organization that you cannot really do that. So you, I suppose we’re seeing incremental changes, maybe, uh, some bigger jumps here and there, uh, when we change things, when we learn information. But yeah, so the two things would be ‘let’s define what is efficiency’ and, and then, ‘let’s measure’. Um, very quickly, and then I, I’ll, I’ll share, I’ll give you, uh, Joan the, the opportunity to, uh, to complete. Uh, for me, efficiency, I, I like to take the, uh, the, the lean approach, which is do, do more with less. So it’s, uh, more deployments, um, do more, have more throughput. Um, so more of, of the business impact that we want to see with less waste, right? So less handovers, less waiting times, less blockers, less defects, less of all the things that slow us down and reduce or subtract from the value that we are, we’re delivering to customers.
Kovid Batra: Makes sense. Joan, uh, over to you.
Joan Díaz Capell: Yeah. I mean. I like the beginning that what Mario said. I mean, I think that the first thing to, to do here is, uh, define what is, uh, efficiency for you as well, what are the.. Because I’ve been there in different companies that we said we will be more efficient, but what does it mean? Right? Somebody put some kind of metrics there, here and there, but, and we align it on that, are we clear with what is definition for, for, from, from all of us? Because there’s different, there could be different people who sit in different ways because they have different lives inside the company. So I would say that the first thing is to, to have this definition of, of efficiency, right? To see which are the, the, your pain points, right? And then try to see which are these pain points and then trying to, to attack them one after the other one. These kind of leverage points that people usually say that in the system thinking, um, because once you have this, then you can try to, to work around them. Um, and I think it’s also clear what, what Mario said, this kind of benchmarking, trying to compare you with, uh, with you. And this is something that we were discussing with Mario as well, another chat that we had, as I used to work for a company that we were doing software for hospitals and we were doing, we were creating some benchmark for different hospitals because we were not, I’m saying that because we used to compare with all these benchmarks that I think accelerate people also, uh, share with all of us. Like, this is how Meta, Amazon and other companies are working. And I see some companies that they’re trying to compare with them, but are you at that level? Are you sharing the same pains of Amazon, Meta and et cetera? I mean, could be, but it’s not, not every time. Usually, usually not. It’s not the situation. So try to find which is the best benchmarks also. Try to find it better. You can, you can easily compare from your side, from your past year, so it’s kind of easy for you if you know which are the metrics that you want to, to measure. So I will say that start with that. Try to compare with yourself, try to see if you’re better. So you are then moving us what you define as efficiency, so you can, we can see then, uh, um, an improvement.
Kovid Batra: Totally makes sense. Do you have any example to share? Like, Mario just touched on that surface, like it could be more deployments that have started happening. There could be less defects, uh, less of handovers, time-based stage, all those things, right? So similarly, if there is an example in your mind where you would apply certain metrics. Can you share that with us?
Mario Viktorov Mechoulam: Yes, yes. I feel I, I’m always going to the same, which is cycle time, but I think this is one of the best metrics that we have. Um.
Kovid Batra: Okay.
Mario Viktorov Mechoulam: So, so being able to reduce the cycle time, especially the higher quantiles, higher percentiles means a lot, uh, means that we as a team are able to focus on everything that we have committed to do. Uh, every time we put something or we start working on something, there is, there is a commitment. So it’s not, not fair to say after a while, ‘oh, this is no longer important because there is something more important.’ And leave it, we leave it hanging out there. Or, ‘this is blocked because, oh, we have to, a dependency with this team and they cannot collaborate right now, so we don’t care.’ No, that, that’s not the correct, or maybe ‘this person is off, so nobody’s picking up the work.’ It’s teamwork, right? So cycle time is team cycle time. So when I see cycle time going down, especially higher, higher percentiles, it means that we’re getting better at focusing on, on our commitments and we are also becoming a bit more predictable, uh, for the work we’re doing. And I, I, I’ll always remember this fun story. It didn’t happen so long ago, but we are looking at the board, right? And you could see, uh, your typical columns, uh, in progress, waiting for review, review, waiting for validation, and validation pipeline, et cetera, et cetera. And, and we were looking at some tickets that were over there, some pieces of work, and, uh, they were stuck. Uh, they were stuck for different reasons. So I asked the question like, who, uh, who’s, who’s responsible of this ticket? Oh, that, that guy that’s assigned to it. Who’s responsible of this one? Oh, this other guy. Uh, no, it’s, it’s the team. So you need team effort, um, to move the work forward, uh, to prevent these, uh, leakages, this waste. So you’re optimizing the system. And, and there are certain statuses, especially the waste statuses that should be tending to zero. The statuses that you can automate, you should be tending to zero. Um, so yeah, when, when, when you look at all those, you can say with certain frequency, may, maybe we are doing, maybe we, um, whether we are delivering more or less value, that, that remains to be seen because, uh, it’s one thing to be efficient. It’s very different to be effective. So you could be very efficient at doing a lot of very good stuff, but then nobody cares about, so you’re, you’re not making any, um, you’re not making any money out of it. But, uh, the first thing that we need to, to make sure is we optimize that, we reduce waste, and we are seeing work, workflow. Um, and then you, then you start getting to these, these times that, uh, these cycle times that are really, really surprising. Um, and by the way, I fully agree with what Joan said. So we don’t have to go to these benchmarks and trust them blindly. There’s, uh, there’s so many, even, even in hospitals, what you would say, like all hospitals would scale in a pretty similar way. Uh, even then, uh, Joan, Johnny, his, his company, they, they did different benchmarks tailored to the size and the context. So we should be doing that for software companies as well. So the one benchmark that you can always do, or that you can always use to compare is, is with yourself. So first step is always am I, am I getting better with my former, uh, self or my former, uh, my, my team one, one month ago?
Kovid Batra: One important thing that I noticed in this use case, uh, the user actually specifically talked about the metrics that, uh, which the engineering team itself should be looking at. Like we talked about cycle time, deployment frequency, handoff times, and uh, reducing these stages. But also, uh, the user mentions about telling it to the executives who might not exactly relate to some of these metrics and would not be able to actually make sense of, of it in terms of the business. So do you think in that situation there would be, uh, specific things that you would like to explain to the executives and show specific metrics? Like, one example comes to my mind is like, instead of telling cycle time particularly or any phase of the cycle time, like the review time has gotten better or the merge time has gotten better, can we just like talk about the overall epic cycle time? Or maybe the lead cycle time for one story to another on an average, how it has improved. So is there any thought on that part? What it, what we should be communicating to the executives?
Mario Viktorov Mechoulam: Definitely, definitely. When, when we talk about cycle time, uh, maybe the only exec that is interested in that might be the CTO, uh, but, uh, for the rest of the execs, we, we need metrics that are bit more tailored to them. Uh, so sorry, metrics are the same, but, uh, let’s say, uh, the granularity at which we, uh, we share with them, uh, share them with them is a bit different. So, as you said, for execs it might be much more interesting to see, uh, how, what’s a cycle time for a, for a feature, right? Um, how, how many features can, can we ship? How likely are we to ship something that we said we are, uh, we are starting today? Uh, so if we can tell them, we can put something in the hands of the customer, uh, with a 95% confidence in one week or one day, that, that’s big, that, that’s very big.
Kovid Batra: Makes sense. Uh, Joan, uh, do you have anything to add on that point?
Joan Díaz Capell: No, indeed. I mean, I 100% agree with, with, with, with Mario. I mean, um, and what you’re saying also, Kovid, because I, from my experience, what I see is that usually engineering teams really focus on their improvements, time to be every time better and better. Um, and we are part of a whole, right? So, uh, at some point are we improving something that is already working fine? I, I don’t know because we don’t see the rest. So for me, makes totally sense that maybe improving on the engineering, we are part of the, of, of a suboptimal, suboptimal optimization. So if you increase a little bit more review, you start also considering collecting and being, as I said before, kind of curious about what’s happening on the epic or feature level, whatever you want to call it. And maybe you get, you get more information about all the whole process because maybe you are, slower than, you are slower because, um, there’s some issues on product or on design or, uh, another stakeholders or some communication that it’s making a, slowing you down. So if you realize about all this process and you understand that there’s kind of a bottleneck, that it’s outside of the, uh, that the regular, um, metrics that you are taking, like, um, how fast you are deploying or something like that, maybe you improve all the, all the, all the, all the flow on the epic or feature level. So you are able to, to reduce some waste. You are able to be more focused on this feature and at the end maybe you have happy customers.
Kovid Batra: Yeah. Makes sense. I think that’s, that would be one right way to explain also to the executives. Great. Uh, guys, I think, uh, this, this point was pretty, uh, well explained by both of you. Uh, in association to this, I think there is one more use case, uh, that we have encountered, which is around the cost benefit analysis. Somehow it relates to the impact of improvements, but this is specifically talking about the cost benefit analysis. I’ll, I’ll explain the use case and I think then you guys can comment on it. Uh, yeah. If an, if an executive, uh, would like to understand the cost benefit of an infrastructure development initiative, like anything that you have done at the infrastructure level and continue to monitor it as it is being developed and rolled out. The initiative is either supposed to reduce the development cost for new products or provide more bandwidth for engineers to take on more projects. So, uh, which metrics could provide such a capability to, to this executive? And like, I would like to understand that part in detail. Uh, I hope the question is clear. Uh, do, do you guys want me to repeat it?
Mario Viktorov Mechoulam: No, it, it is clear.
Kovid Batra: Okay. Okay.
Joan Díaz Capell: Mario, go ahead there. You’re the senior here so you can start the conversation always and I’ll follow you.
Mario Viktorov Mechoulam: Okay. Okay.
Kovid Batra: Uh, alright. Alright. Like, let’s go ahead with Mario then. Yeah.
Mario Viktorov Mechoulam: Thank you, Joan. Um, so, so when I hear this, uh, to me, um, so reducing costs or freeing more time to do all the stuff is two sides of the same coin, right? Did, did I get this part right in the question? Yeah. Okay, so what, um, so what the use case is, people want to understand how, whether this, this cost reduction or this, uh, saving of time is being materialized or not. So yeah, here, that, that’s why I think it’s very useful to have people in, in the company that, that, that can help around and, and ask the right questions when these initiatives are started because, um, so if somebody came to me with this question, uh, my, I, I would, so my first question to them would be, alright, so you’re doing something, let’s call it ABC. Uh, because probably you’re expecting to see something, so this something you, you should have defined and then you might be right that you’re gonna see that or, or wrong, uh, and you might see additional things. So, uh, for example, if somebody is talking about infrastructure improvements, this is what, uh, this, this is what this customer was asking about, right? So infrastructure improvements can be many things. But let’s say that we are investing in, uh, I don’t know, uh, moving out of our old CI/CD system, uh, introducing, embracing some more competitive solutions out there so that we can, we reduce the gap between the, uh, the moment where the code is, uh, pushed to the, to the master branch and when it’s in production. So putting in, in, in the customer’s hands earlier. Um, I think that, uh, this is one way which we can see has these, um, cost reduction being, being materialized, uh, economical cost. I won’t go there because I think it’s very straightforward. So if you’re replacing technology A for technology B, you’re doing some optimizations here and there and you expect to see some cost reduction, that, that’s very easy. You can see that in the, in the finance, uh, financial, um, sheet. Um, what, what else? Um, so we, we might look at other things like, uh, can we do more? Uh, so how do we free the engineer’s time? So now that, uh, so that now we are doing more and maybe we want to measure that with deployment frequency, uh, however with deployment frequency, it’s, uh, it’s always a bit tricky, right? Because, um, first you need to know whether, whether you have a bottleneck. And, uh, there is an engineer I, who wrote an article I love, a public writer, uh, from, from Hotjar. Um, he, he, he explained how they, they improve the, um, the value of flow at back at Hotjar because they had a bottleneck in their, uh, deployment pipeline, right? And, and they could see that because, uh, the article is out there, it’s very, very, very, uh, recommended. You could see that because they were hitting this bottleneck, so they could see some consistent number of deployments per day and acute. Right. But, um, perhaps when you change the technology and you have introduced these optimizations, uh, you can do much more deployments per day. Perhaps you have a spike. So one day you have 20 deployments, but maybe the, the second day you have two. So you have to keep this into account if you, uh, you really need to have, to have something to deploy. Um, so in, in this case, I would look for, um, maximums. So maximum number of deployments in a given period, daily deployments in a given period to understand if you have removed the, uh, a constraint that, that existed or not. Uh, and that’s enough. So you don’t have to see that every day, this deployment frequency, because at the end of the day, unless you are like the high performant elite team in some benchmarks, you won’t be having, uh, uh, 20 deployments per day. Maybe you do some days, maybe you don’t others.
Kovid Batra: Totally, totally. Joan, do you, do you have an example to share, uh, where any kind of infra improvement impacts the cost and defines the benefit of doing that?
Joan Díaz Capell: Um, not a particular one, but when Mario was talking, I was relating it, uh, again, with the, the topic that we were discussing at the, at the beginning, right? So you have, you should have to be clear about what are the things that you would like to improve at the beginning. So we are talking again, maybe as, as efficiency thing, again, because you want to reduce something or, or increase, I mean, increase the revenue or reduce the cost. Uh, you can try to do both of them or at the same time, you should know first what, what are the things that you would like to do, why you are doing that. You are not doing that because there is a new technology, right? So you have kind of, first of all, you have to be clear with, I would say that you have to be very clear with, um, what you want to improve. And then if you are, you have this hypothesis, then you can check later on if this is happening. Um, or not. So you can then, as Mario was saying, also free up some time then that maybe people can, can use in order to investigate new things, et cetera. But again, I think that it’s kind of understanding what are your problems, that you are doing in order to be more efficient as well, more than, more than solving an issue because you think that it’s the, the way to go.
Kovid Batra: Makes sense. Uh, there, there is one example that I could remember from this discussion. Uh, somebody on LinkedIn had posted about it. Uh, so they said if you are doing some level of improvements, if you’re getting rid of your tech debt, then one good metric to look at would be, uh, the first line of code, uh, that your new developers comes and pushes it. So the time taken, uh, for a developer to commit the first PR should reduce. Basically, that’s how you can gauge, uh, that whether you have improved something or not. Of course, that was very situational and specific to one scenario, but I think this is also one place where we can see how fast people are able to push the code to production. If you have gotten rid of some level of, uh, tech debt or you have done some improvement, automatically those PRs would be getting pushed faster. So yeah, I think that example just, uh, striked me. So I just wanted to mention it.
Mario Viktorov Mechoulam: Yeah, this is a very good example. And I, and I would tend to agree that most of the cases you, it’s, it’s a good predictor. So it can tell you how, how big a repository is, uh, how coupled is the coding site, how complicated, uh, are long, are the, the, the methods, uh, and the different files. It can tell you how, how good is documentation, whether there is shared knowledge between your senior and non-senior engineers in that team or in other teams, uh, your pipeline for deploying data production, quality gates, anything that you could have that can give a quick onboarding and one of these to ship the software with confidence. It’s, uh, all those, uh, yeah, would be, would be great things to, to look for.
Joan Díaz Capell: And also, on top of that, something that, that we usually like to say is that every single line of code that you do, that any user executes, it’s waste till somebody executes it on production. So, so taking a little bit of the lean, uh, mindset here is, uh, are you producing a lot of waste or, or, or not? So the, the less waste that you create, meaning that the more lines that you are doing are there in production and being executed by a user, the better as well, so.
Kovid Batra: Yeah, totally, totally. Perfect. Perfect, guys. Uh, I think we can move on to the next, uh, use case. And I think this is a big, big, big pain point, uh, for engineering managers, for product managers. But let’s, let’s just stay specific to the engineering teams right now. And the pain is, uh, telling, uh, the executive, telling the product team, the business team, when exactly are we gonna deliver? What’s the feature delivery time? Predicting that. And honestly, uh, in my career, I haven’t seen, uh, a robust way, a very predictive way, uh, that would be very accurate. But still, there is something that I came across recently after talking to Mario, and I thought it would be great to bring up this point. So I’ll highlight the use case. I’ll explain it so that the audience also relates to the use case, and then you guys can continue on that point. So it’s the case where the team needs to be more robust. Uh, they need a more robust way to predict the feature delivery time, uh, considering the inherent uncertainties, uh, in individual tasks, uh, there is no accurate way to do it, and a single point of estimate is likely to be very inaccurate. Uh, so a broad range of, uh, broad range might be, might not be helpful for planning. So anything that you guys think would be helpful here? Any methodology, any framework that would be helpful here. Let’s talk about that.
Mario Viktorov Mechoulam: Good. You want me to start again, Joan?
Joan Díaz Capell: No, I think I can start because come on, come for me. I can, I can just now. No, I think that this is a never ending, uh, story, because, uh, we use, I mean, I like to say that, um, by definition estimation isn’t accurate, right? Because otherwise it’s not an estimation. So sometimes we try to, to, to spend a lot of time and trying to understand the, the problem, the issue so we can do a better estimation. And what does a better estimation mean? Uh, spending more time and not to understand better. So sometimes we end up in, I have the feeling that in my experience, we end up in a situation that I’m not sure if we’re investing or wasting time in order to give a better planning. So, um, one of the things that we, I mean, I think that we already started working with Mario, I think it was like six years ago, something like that. It was with, with this Monte Carlo, um, uh, model that, uh, I think that the name, what was the name? The name is coming from, from this random nesting. They in the casinos.
Mario Viktorov Mechoulam: Yeah.
Joan Díaz Capell: Um, but we will talk about Montecarlo maybe later on, in, in, in order to go deep, uh, on it. But we, what we were trying is, uh, moving out of this more deterministic estimation, like you said, okay, we’re gonna deliver this particular day. We’re trying to move more to a, more probabilistic forecasting way, like, uh, understanding our current situation, we have, uh, 80% probability that we will deliver this thing, I don’t know, in two months or before. So then you can, more, you can also manage the risk there because it’s like, okay, it’s 80% probability, but a 7% probability is what? Well maybe, uh, half months. So we can have this discussion also with, with, uh, different stakeholders. And also, um, because usually when we give estimations, we are very optimistic. And to be honest, from my experience, when you say an estimation, you are giving the average because it’s more or less, I think that the worst scenario, it’s 4. Better scenarios, 2. So I don’t know, 3 plus, 3 days, something like that. So we usually work on estimations and an average, sorry. And you know, that average rates are 50% of the time wrong. So it’s, um, it’s a lot of, um, putting a lot of pressure to, to an, to a, an estimation. So, um, for that point, we, this is when we started trying to, to, to move to the, this probabilistic, um, estimation that maybe, I don’t know, Mario, if you want to go deep on, on, on that experiment, uh, we did and we continue running in our companies that we work for.
Kovid Batra: But before, before we go, uh, deeper into that part, uh, you mentioned Monte Carlo. Uh, where is this name coming from? Uh, what is it about?
Joan Díaz Capell: Um, I think it comes from, from casino and these kind of randomness things because, uh, at the end it’s like, I mean, you collect all the data that you have and you dice, uh, uh, you roll the dice, sorry, you roll the dice like, uh, thousand of times and you collect all this information. So based on not all this information, for instance, you can do it on the story points, you can do in number of stories finished, on lead time, all this stuff, we will live on that. So, um, at the end you build a model based on your past data so you can understand which are the probabilities that are based on the behavior that you had, if they’re going to be repeated or not.
Kovid Batra: I think Mario also has done some research on that. Uh, do you, do you have something on, on the Monte Carlo thing? Yeah.
Mario Viktorov Mechoulam: Yeah. You want on the name or on the, on the method?
Joan Díaz Capell: Both.
Mario Viktorov Mechoulam: I, uh, actually, so yeah, I, I think, I think the, the creator of the, or the, yeah, the, the person that first documented this, uh, Monte Carlo probability forecasting, he had an uncle that had some gambling issues. Uh, Stanisław Ulam, I think it was. And, uh, and that’s why he, he ended up choosing, uh, choosing the name. But, uh, but I, I like how, um, I like much more, uh, not the, the gossip or the trivia, but, uh, the explanation Joan, Joan gave, I think it’s much more accurate to, um, yeah, to think about this, uh, probabilistic model because of the randomness in, in the dice rolls. Um, I, I, I love how Joan introduced this, by the way, uh, moving from deterministic to, uh, to probabilistic. Uh, also, so one, one thing he mentioned is that we normally give, um, a scenario which is the, the average when we give estimates. I would adventure to say we usually give the optimistic scenario, not, not even the average. And you can make this example. So you can ask the team about an estimation and are you sure? Yes. And then after a while, ask them, okay, so if everything goes super smooth and super well, what’s your estimation? It’ll be the same. It’ll be, it’ll have a 5% rate. So that’s when you realize that there is at least 10 or 12 variables that you do know, you’re aware of, that can each of them go wrong and each of them can have an impact on others. And there’s probably hundreds that you don’t, are not even aware of that, that can happen. Um, there was this, uh, from a pro-Kanban site, uh, I think it was the, uh, planned likelihood, likelihood. They had this simple, uh, estimator on the website. Now I think you have to register to see it, but you could simply add each of these variables, uh, that could have an impact in a, in a, in an estimation. And the likelihood of these, uh, happening or not happening, uh, you could see once you started out three, four things, like somebody going on a on a, I dunno, breaking a leg, uh, somebody leaving the company, uh, a new urgent thing coming up, you could see how your, your plan suddenly fell into pieces and didn’t make, uh, any, any sense anymore. So I, I love Monte Carlo forecasts, uh, because of that, because they, they take a real scenario and just give you a window, a spectrum of opportunity, a spectrum of, sorry, of delivery dates and likelihoods. And then with that information, I think stakeholders can get a much better understanding of first, what is a team going through? What’s hidden between the day-to-day world, the context which, the technical debt, uh, the people issues that there might exist. And then how, when, when can I realistically wait for something that’s important for me to be done? And that also forces you to do some, uh, to make some decisions, right? Because if 10 things are important to you and you, you start 10 things in a team and you see when, when you’re gonna receive that, uh, well, you won’t like it. Uh, but then you realize that if you choose one single thing, uh, maybe the plan looks much more optimistic now. So forces also stakeholders to make decisions on, um, on priorities. Um, what else? Um, there, there, there is a pitfall here, I think, uh, that we, we did with Joan, uh, in our early days using that. Uh, so we, we loved it so much and it turned out to be so accurate, and we can go afterwards to explain why this is accurate and why more and more teams should adopt it and, and stop wasting time on estimations and another, um, other promises. Um, but the mistake, the mistake we made at that point was, um, we, we started to trust it and like it, so at some point somebody handed us a project that was very urgent, very important, and we took, took, we, uh, Joan introduced the numbers and we said, okay, three weeks. And we said, uh, I don’t know, to the CEO of the company, it’s gonna be three weeks. And with no, no, no explanation, right? So just, just the, uh, I think we gave the 85, uh, percent of likelihood for this being done. Um, so what happened, our, our, our manager came to us and told us, like how is this possible? Sorry, what, what the hell guys? This is very important. Why, why you have to say three weeks? So we sat down and explained like, like, look, there is this, we have to do this work and then A, B, C, and then one to three, and there is this problem and there is this person that’s away, and if you look at our, our throughput, uh, this, this is the most likely date. And then it was like, oh, wow! Of course, it makes sense, but you should explain that, right? If you don’t explain that to the, to the stakeholder, it is gonna be very awkward because it’s gonna generate mistrust.
Kovid Batra: Makes sense. Makes sense. Would you like to add something?
Joan Díaz Capell: No, I would like to add, to add something on, on, on top of that, uh, before, um, before, I mean, because maybe using Monte Carto at the beginning, people can be very frustrated because it’s like, okay, a new framework, I have to learn something, et cetera. I would like just to encourage people like, uh, with this probabilistic um, mindset, um, you can do a lot of things yet without Monte Carlo. Still, I mean, as the very beginning, as I said at the beginning, as a baby steps, I mean, I’m gonna share also some examples that I use with most of the teams that I work with is like, if you have a good structure that we talk already with your features or, uh, epics and you know how you’re delivering them, there’s a very easy, I mean, um, I was taking the throughput of the feature that we were delivering per queue. And it turns out that in one queue we, uh, we released two features. Another one, three features and another one five features. So with this information, I already know that it’s very unlikely that we deliver less than two features, and it’s also very unlikely that we deliver more than five. So we, with all the, this simple information, I already have something that it’s very likely that they will deliver something between two and five. I know that the size matters and we can, in this case, it’s something that we can discuss also, which is the right sizing, which is the expectation of the, the size. But only with doing this thing and not going very deep into Monte Carlo, that it’s kind of a next level, with only this information, you can already start planning and you can give a lot of information there.
Kovid Batra: Makes sense. And I think that’s a very good advice because it’s true, uh, when you’re involved with so many other things on the delivery and like getting things done, uh, introducing a new framework that brings in another level of complexity to find out what it’s gonna get on time or not, might be a little, uh, inhibiting. So it’s good that we start with a very simple probabilistic model as you just explained. And then probably with better and more scaled projects, with more scaling of teams, we can bring in the complete Monte Carlo in place. Great, guys. I think, uh, that, that really helps, uh, at least bring and throw some light on how people should be approaching towards, uh, probabilistic, uh, timelines and not just being deterministic about it. Uh, there is another use case that I want to cover today, but it seems that we are already running, uh, short on time that way. So I would request like we, we start with the QnA, and I’m sure someone is gonna ask about that use case within the questions. And if not, I’ll ask it as a question.
Mario Viktorov Mechoulam: Alright. Okay.
Kovid Batra: So, uh, the audience is waiting. Uh, I’ll just take two minutes of break, uh, for, to have water and then, uh, we’ll start with the QnA.
Mario Viktorov Mechoulam: Okay. Uh, Kovid, I think we also, because this use case is very connected to Monte Carlo and we just started scratching the software, perhaps we can dedicate a session just to talk about it and all the, all the things that we can do.
Kovid Batra: Yeah, totally.
Mario Viktorov Mechoulam: To help this.
Kovid Batra: We can do that. Definitely we can do that. Alright, uh, let’s break for the QnA, uh, two minutes break and then, uh, we’ll join in.
Alright, we are back and we have a few questions already coming in. Uh, oh, by the way, Clint says hi, uh, Mario.
Mario Viktorov Mechoulam: I saw that. Yeah.
Kovid Batra: Perfect. Okay, let’s start with the first question. Uh, that’s come, that’s coming from Saloni. Uh, what are the most important metrics to track if we want to control tech debt? Okay, interesting question. Uh, like if we are looking at tech debt and we want to keep it under control, what metrics should be focused on? Uh, and what metrics we should be working on? Uh, is the question. Mario, would you like to take it?
Mario Viktorov Mechoulam: Sure, sure. I, I think there’s a few metrics that, uh, that people regularly track. Um, and that is, um, the, the, the size of the pull request, for example. So, um, if you have a lot of technical debt in the code, your code might be more convoluted and force you to, to do bigger pull requests, uh, then the defect, uh, rate as well. Uh, because more complicated code, uh, with more technical debt can, uh, inadvertently force you to introduce more bugs, et cetera, et cetera. So there, there is a bunch of those that people have traditionally used. Uh, not long ago I saw something that I loved and I’ve been trying to introduce to my team with not a lot of success yet. So it is there, nobody has pushed back, but it just, it hasn’t had enough traction yet, but it is, uh, tracking the days, uh, since the last time you worked on technical debt or technical health, right? So if you see that consistently at zero, uh, so, or like, sorry, uh, increasing ’cause we’re tracking amount of days since the last time you did that, you have a problem. So you want to make sure that you are, uh, consistently dealing with, with technical debt and you make sure that that is part of the, the way you work. Um, if you see yourself also, uh, planning time for technical debt, uh, you can do better. So you should be able to do that as part of your feature development, as part of your day-to-day work. You should be able to do boy scouting role. So I see this, I know I have, this is important. I’m gonna go and fix it, and things should be able to wait.
Kovid Batra: Totally. I think, uh, we didn’t touch upon that topic of capacity allocation, but there is one, uh, thing that I also have seen is that within your capacity allocation, the work that the teams are picking up, if the technical debt seems to be like normally between 15–20% of every sprint, or every month or every quarter, whichever timeframe you’re looking at, then also we can keep things under control. So in general, uh, a good number, a good benchmark would be your 15–20% of time effort allocation should be towards tech debt, we should be continually improving on that. So just adding to what, uh, Mario, you said, Saloni, to answer your question, uh, this could be a good metric to look at. Have a look at a broader picture of the capacity allocation, and then within that 15–20% should be on the tech debt. Uh, Joan, do, do you have any opinion on that? Any thoughts, any metrics that you think we should be looking at?
Joan Díaz Capell: No, uh, actually I like pretty much what you said, Kovid. I mean also, yeah, in terms of, I think that tech debt is very important. You have to, to keep them in a good shape. Uh, so you have to pay your debt. So, this means that, this means that, uh, and I like that you said this capacity allocation, because we usually, with Mario, when we say capacity allocation is, uh, pieces of work, not people, uh, not people at all. So, I think that having this kind of service dedicated to that, I think it’s, um, the best that you can do also, that you are in your debts day after day and you’re working on it so you don’t forget about it. It’s completely..
Kovid Batra: Totally, totally. I think what I, I really liked what Mario also mentioned about the size of the pull request. Of course that can, uh, tell a lot of other things as well, but size of pull request in general, if it’s large, definitely there is something going on. Uh, and it could be related to technical debt also. Great. Uh, great question, Saloni. Thank you so much. Uh, I hope we answered it for you. Uh, next question, guys. Uh, this comes from Madhurima. Uh, hey, Mario. I have a question. Uh, what kind of qualitative data should, we should collect alongside metrics to ensure better visibility and planning for engineering? Uh, I think a very interesting question. Uh, like we always look at the objective things, the numbers and everything. Uh, but along with that, what qualitative data would help engineering teams, engineering managers, uh, to make better decisions when they’re planning for their teams?
Mario Viktorov Mechoulam: Mm-hmm.
Kovid Batra: I think, uh, Mario, uh, this has come to you specifically, so you can start.
Mario Viktorov Mechoulam: Yeah. Interesting. It’s like if they, they knew me. Um, it’s a very good question and I’m not sure I have a well prepared answer for that, but, um, I would like to connect this to tracking some form of wellbeing. And I wouldn’t do that just for, uh, visibility and planning. Uh, that definitely can have an impact. I would do it for everything. But if you have your team consistently being unsatisfied with the type of work they’re doing, uh, feeling burnout, uh, working over time, not feeling that they have the time to do their best, forced to introduce technical debt, et cetera, et cetera, so this will, of course you have metrics for this, uh, that are quantitative, but this would, would reflect in their wellbeing, in their engagement, in their motivation, in, in if, uh, in if, if they have the opportunity to work on things they love, that they can put their strengths and motivation in. So I think you should track that because otherwise many things that might otherwise look like, oh, it’s the perfect plan with the perfect delivery date with the, uh, I don’t know, um, might, might suddenly fall into pieces. Uh, not, not because, uh, the, the way you, you, you use for building the plan and forecasting and, and dealing with the day-to-day of the team is wrong, but because of simply their wellbeing.
Kovid Batra: Makes sense. Makes sense. Uh, Joan, would you like to add?
Joan Díaz Capell: On the qualitative realm, maybe, uh, maybe I will try to ask something like, um, are the goals clear for you? Maybe, um, do you have any pain during the planning? Um, I don’t know, what else? Um, do you think that what you’re delivering makes an impact? Something like that. But these are not the questions, I mean, don’t take the, that question for granted. Like these are the questions that you have to take, to do, uh, or to ask. Um, I’m taking these questions because I think that, that some company that I work, they were kind of the pain points that some people were complaining, so I would say that when we talk about qualitative data, I would say listen to people, try to understand what are the pain points that they have related to that particular topic and bring it into your data, you know, to see how they are improving. So they were taking back the benchmarking thing again.
Kovid Batra: Makes sense. Uh, the next question, uh, this comes from Aksh. Uh, hey Joan. Uh, my question is that we are dealing with delays in and out. Most of the time it’s because of cross team dependencies and handovers. Uh, how can we track this and optimize for it?
Joan Díaz Capell: Well, what’s in front of a complex problem? So there’s not a simple solution for that. Um, so again, um, when there’s this kind of, how do, what do you do with these kind of dependencies? Do you manage dependencies or do you break dependencies? So I will go first to go deep on that, to understand why do you have these dependencies? Because if you are managing dependencies, you will always have delays, to be honest. So first of all, I will try to see if you can break these dependencies. Breaking dependencies, there’s a lot of things. I mean, I don’t know. I’m gonna make it up. Okay? Maybe they are silly or not, but maybe you have a front end, a backend team and doing these things, things together. Maybe you can mix them and have a functional team or not. Maybe you have a team that is more focused on a particular product and another, another product, and there are dependencies. They have dependencies, but maybe they, you can do the other team that is this product that they’re working or they are sharing some kind of technology. I would go first, first with that. If you cannot do this or this is not possible, I would say that, um, with all the things that we were saying, all these models like understanding the right size in your epic level, um, forecasting, et cetera. This is something that you can visually see. And this is these kind of models that, um, forecast your delivery. Something that with just one click, you can execute. So this is something that you can day after day track and manage it, and you can improve a little bit the, the, the, the pains that you’re having on the, on the, the dependency level.
Kovid Batra: Makes sense. Makes sense. Uh, Mario, uh, would you like to add?
Mario Viktorov Mechoulam: Just one thing. Uh, I think Joan’s answer was spot on, but, um, he once told me something that I think is, is perfect for this situation. Not, not if you just want to track it. So if you want to track it, you have ways to track, uh, waiting, handover, blocked time, review, whatever. Um, but if you, if you want to, to improve it, to fix it, to prevent it, as he said, breaking the dependencies. Uh, I, I would, if you are, if you have the power or if you, at least if you have the influence, I would move those teams closer together. Because if you have teams that are part of a bit of a wider group that share OKRs, then suddenly, yes, you have dependencies. But these dependencies will more likely be collaborations. You’re both working towards the same goal at the same time, because normally your OKRs have, uh, like these windows, three to six months, uh, maybe even a bit less depending on how you, how you do them, right? So by moving the teams closer, um, you have them working on the same OKRs and you probably have much more opportunities for touch points and synchronizations just naturally, right? Just, just as part of how the group works together or, or this part of the organization.
Kovid Batra: Makes sense. Perfect, guys. I think, uh, one last question we will take, uh, that’s from Nisha. How do we make sure that there are not too many work in progress items for the developers? Okay. That that’s..
Mario Viktorov Mechoulam: That’s always the way.
Kovid Batra: Yeah, that’s always the problem. But yes, uh, let’s, let’s answer it to our best, how, how we can, uh, get rid of that or improve upon that.
Joan Díaz Capell: One. One of the things that, uh, I used to, I mean, I tried to, this is the best ever thing, but one of the things that, uh, I tried to do is we were discussing about high level, uh, pieces of work, like more epics and features. So what I’m trying to do with most of the teams is that at the very beginning is we are working in one feature, so just lot of focus on this feature. Just the, the thing that we are limiting the WIP on a feature level, you are already impacting on the WIP that developers get, because if you start working in 10 features, that’s even more complex to jungle around, trying to play some, play some tetris, et cetera. So I would say that at the beginning, start putting your focus on limiting the WIP on the epic and feature level. And then you will see, you can add more. But first, limiting this, I would say.
Kovid Batra: Makes sense, Mario.
Mario Viktorov Mechoulam: Great, great. Um, I think this is the way to go. Uh, if anything, I would add some, some, so if you want to put some numbers on it, there is, uh, an experiment, uh, we’ve been running, uh, for a while, and that is, uh, you probably want to have less items in progress. And not talking about feature, but just the, the database, small tasks or whatever.
Kovid Batra: Yeah, yeah.
Mario Viktorov Mechoulam: Uh, less, less than number of people in the team. Number of engineers. Why? Why? Because if you already have some number of tasks that are, that is higher than the amount of people in the team, people are context switching, people are multitasking, things are waiting, you’re generating waste. You’re doing badly. But if you have the same size of, uh, the same amount of tickets and size of the team, it means that nobody’s collaborating or collaborations are very, um, unfrequent and maybe just happens through pull requests. But you, you would want people to be, uh, helping each other, to be doing pair programming, to be, uh, having brainstorming sessions on how to do this change in the architecture, uh, what happens with the design. So you probably want to keep that number always lower than the number of engineers.
Kovid Batra: Makes sense. I think that’s, that’s the magic trick here. Uh, like if we look at that metric, it would definitely make sense and always the work in progress would be not too many, it would be limited and people will be able to collaborate, get time for it. Makes sense. Makes sense. Alright. Uh, see guys, uh, we planned for more things to talk about, but unfortunately we’re not gonna do that today. Fortunately, if Mario and Joan are available sometime this month again, we’ll bring them back on this webinar and talk about, in depth about Monte Carlo, talk about capacity utilization and few more things that are already in the pipeline. So for today, uh, and that’s our time, that’s our time. Uh, but we’ll definitely join in for the next session. Uh, one thing before we leave, uh, we always, always want you guys to share some parting advice with our audience. So again, I will go with Joan first. Uh, Joan, uh, any parting advice for the engineering managers and the engineering leaders listening out here?
Joan Díaz Capell: You always, I think that I, I can finish with what I, uh, with what I start at the beginning, like, uh, curiosity. I mean, be very curious. Um, I like to say this kind of be like a beginner’s mind, so put everything in, uh, into a question and try to understand why all these things are happening. Don’t take anything for granted. I mean, just be very, very open and, and be, I don’t know, question everything.
Kovid Batra: Good piece of advice I think. That’s, that’s the first step towards making any kind of progress, because only when you explore, you understand what’s out there to improve upon and do things. So totally makes sense. Mario?
Mario Viktorov Mechoulam: To expand on that and to connect with the drainpipe with the initial questions on framework, um, uh, there’s a lot of things out there. Um, so don’t, don’t jump on the first thing that somebody, uh, sells to you in terms of framework. Um, you, it’ll take you some time to feel comfortable with what you see and to become an expert, and you’ll do that by, by experimenting as Joan said. So, start small and build up from there.
Kovid Batra: Totally. Great, guys. Thank you so much for your time today. It was really nice talking to you. Uh, we would love to have you guys back to talk more about such things. Uh, but today it’s goodbye. See you again. Thank you.
Mario Viktorov Mechoulam: See you soon. Thank you.
Joan Díaz Capell: See you. Bye.
How do engineering leaders stay relevant in the age of Generative AI?
With the rise of GenAI, engineering teams are rethinking productivity, prototyping, and scalability. But AI is only as powerful as the engineering practices behind it.
In this episode of the groCTO by Typo Podcast, host Kovid Batra speaks with Suresh Bysani, Director of Engineering at Eightfold, about the real-world impact of AI on engineering leadership. From writing boilerplate code to scaling enterprise platforms, Suresh shares practical insights and hard-earned lessons from the frontlines of tech.
✅ AI Meets Engineering: How GenAI is transforming productivity, prototyping & software workflows.
✅ Platform vs. Product Teams: Why technical expectations differ — and how to lead both effectively.
✅ Engineering Practices Still Matter: Why GenAI can’t replace fundamental principles like scalability, testing, and reliability.
✅ Avoiding AI Pitfalls: Common mistakes in adopting AI for internal tooling & how to avoid them.
✅ Upskilling for the Future: Why managers & engineers need to build AI fluency now.
✅ A Leader’s Journey: Suresh shares personal stories that shaped his perspective as a people-first tech leader.
Closing Insight: AI isn’t a silver bullet, but a powerful tool. The best engineering leaders combine AI innovation with strong fundamentals, people-centric leadership, and a long-term view.
Kovid Batra: Hi everyone. This is Kovid, back with another episode of groCTO by Typo. Today with us, we have a very special guest who is an expert in AI and machine learning. So we are gonna talk a lot about Gen AI, engineering management with them, but let me quickly introduce Suresh to all of you. Hi, Suresh.
Suresh Bysani: Hello.
Kovid Batra: So, Suresh is an Engineering, uh, Director at Eightfold and he holds a postgraduate degree in AI and machine learning from USC, and he has almost 10 to 12 years of experience in engineering and leadership. So today, uh, Suresh, we are, we are grateful to have you here. And before we get started with the main section, which is engineering management in the age of GenAI, we would love to know a little bit more about you, maybe your hobbies, something inspiring from your life that defines who you are today. So if you could just take the stage and tell us something about yourself that your LinkedIn profile doesn’t there.
Suresh Bysani: Okay. So, thanks Kovid for having me. Hello everybody. Um, yeah, so if I have to recall a few incidents, I’ll probably recall one or two, right? So right from my childhood, um, I was not an outstanding student, let me put it that way. I have a record of, uh, you know, failing every subject until 10th grade, right? So I’m a totally different person. I feel sometimes, you know, uh, that gave me a lot of confidence in life because, uh, at a very early age, I was, you know, uh, exposed to what failure means, or how does being in failure for a very long time mean, right. That kind of gave me a lot of, you know, mental stability or courage to face failures, right? I’ve seen a lot of friends who were, you know, outstanding students right from the beginning and they get shaken aback when they see a setback or a failure in life. Right? So I feel that defined my personality to take aggressive decisions and moves in my life. That’s, that’s one thing.
Kovid Batra: That’s interesting.
Suresh Bysani: Yeah. And the second thing is, uh, during undergrad we went to a program called Net Tech. So it’s organized by, um, a very famous person in India. It’s most of, mostly an educational thing, right, around, uh, cybersecurity and ethical hacking. So I kind of met the country’s brightest minds in this program. All people from all sorts of background came to this program. Mostly, mostly the good ones, right? So it kind of helped me calibrate where I am across the country’s talent and gave me a fresh perspective of looking beyond my current institution, et cetera. Right. So these are two life defining moments for me in terms of my career growth.
Kovid Batra: Perfect. Perfect. I think you become more resilient, uh, when you’ve seen failures, and I think the openness to learn and exposure definitely gives you a perspective that takes you, uh, in your career, not linearly, but it gives you a geometric progression probably, or exponential progression in your life. So totally relate to that and great start to this. Uh, so Suresh, I think today, now we can jump onto the main section and, uh, talk more about, uh, AI, implementation of AI, Agent ai. But again, that is something that I would like to touch upon, uh, little later. First, I would want to understand from your journey, you are an engineering director, uh, and you have spent good enough time in this management and moving from management to the senior management or a leadership position, I would say. Uh, what’s your perspective of engineering management in today’s world? How is it evolving? What are the things that you see, uh, are kind of set and set as ideals in, um, in engineering management, but might not be very right? So just throw some light on your journey of engineering management and how you see it today evolving.
Suresh Bysani: Yep. Um, before we talk about the evolution, I will just share my thoughts about what does being an engineering manager or a leader means in general, and how is it very different from an IC. I get, I get asked this question quite a lot. A lot of people, a lot of, you know, very strong ICs come to me, uh, with this question of, I want to become a manager or can I become a manager? Right. And this happens quite a lot in, in Bay Area as well as Bangalore.
Kovid Batra: Yeah.
Suresh Bysani: So the first question I ask them is, why do you want to become a manager? Right? What are your reasons for it? I, I hear all great sorts of answers, right? Some folks generally come and say, I like execution. I like to drive from front. I’m responsible. I mean, I want to be the team leader sort of thing, right? I mean, all great answers, right? But if you think about it, execution, project management, JIRA management, or leading from the front; these are all characteristics of any technical leader, not just engineering manager. Even if you’re a staff engineer or an architect or a principal engineer, uh, you are responsible for a reasonable degree of execution, project management, planning, mentorship, getting things done, et cetera. After all, we are all evaluated by execution. So that is not a satisfactory answer for me. The main answer that I’m looking for is I like to grow people. I can, I want to see success in people who are around me. So as an engineering manager, it’s quite a tricky role because most of the time you are only as good as your team. You are evaluated by your team’s progress, team’s success, team’s delivery. Until that point, most ICs are only responsible for their work, right? I mean, they’re doing a project.
Kovid Batra: Yeah.
Suresh Bysani: They do amazing work in their project, and most of the time they get fantastic ratings and materialistic benefits. But all of a sudden when you become an engineering manager or leader, you are spending probably more number of hours to get things done because you have to coordinate the rest of the team, but they don’t necessarily translate to your, you know, growth or materialistic benefits because you are only as good as an average person in your team. So the first thing people have to evaluate is, am I or do I get happiness in growing others? If the answer is yes, if that’s your P0, you are going to be a great engineering leader. Everything else will follow. Now to the second question that you asked. This has been, this remained constant across the years from last 25 years. This is the number one characteristic of an engineering leader. Now, the evolution part. As the technology evolves, what I see as challenge is, uh, in a, an engineering manager should typically understand or go to a reasonable depth into people’s work. Technically, I mean. So as the technologies evolves, most of the engineering managers are typically 10 years, 15 years, 20 years experienced as ICs, right?
Kovid Batra: Yeah.
Suresh Bysani: Now, uh, most of these new engineering managers or seasoned engineering managers, they don’t understand what new technology evolution is. For example, all the recent advancements that we are seeing in AI, GenAI, you know, the engineering managers have no clue about it. If the, most of the time when there is bottom up innovation, how are engineering managers going to look at all of this and evaluate all of this from a technical standpoint? What this means is that there is a constant need for upskilling, and we’ll talk about that, uh, you know, in your questions.
Kovid Batra: Sure. Yeah. But I think, uh, I, I would just like to, uh, ask one question here. I mean, I have been working with a lot of engineering managers in my career as well, and, uh, I’ve been talking to a lot of them. There is always a debate around how much technical an engineering manager should be.
Suresh Bysani: Yeah.
Kovid Batra: And I think that lies in a little more detail and probably you could tell with some of your examples. Uh, an engineering manager who is working more on the product team and product side, uh, and an engineering manager who is probably involved in a platform team or maybe infrastructure team, I think things change a little bit. What’s, what’s your thought on that part?
Suresh Bysani: Yeah, so I think, uh, good question by the way. Uh, my general guidance to most engineering manager’s is they have to be reasonably technical. I mean, it is just that they are given a different responsibility in the company, but that’s it. Right? The, it is not an excuse for them, not for not being technical. Yes, they don’t have to code a 100% of the time that’s given. Right. It, it, so how much time they should be spending coding or doing the technical design? It totally depends on the company, project, situation, et cetera. Right? But they have to be technical. But you have a very interesting question around product teams versus platform teams, right?
Kovid Batra: Yeah.
Suresh Bysani: Engineering manager for product teams generally, you know, deals with a lot of stakeholders, whether it is PMs or customers or you know, uh, uh, the, the potential people and the potential new customers that are going to the company. So their time, uh, is mostly spent there. They hardly have enough time to, you know, go deep within the product. That’s the nature of their job. But at the same time, they do, uh, they are also expected to be, uh, reasonably technical, but not as technical as engineering leaders of platform teams or infrastructure teams. The plat for the platform teams and infrastructure teams, yes. They also engage with stakeholders, but their stakeholders are mostly internal and other engineering managers. That’s, That’s the general setup.
Kovid Batra: Yeah. Yeah.
Suresh Bysani: And, you know, uh, just like how engineering managers are able to guide how the product should look like, platform managers and infrastructure managers should, you know, uh, go deep into what platform or infrastructure we should provide to the rest of the company. And obviously, as the problem statement sounds, that requires a lot more technical depth, focus than, than the rest of the engineering leaders. So yes, engineering managers for platform and infrastructure are required to be reasonably technically stronger than the rest of the leaders.
Kovid Batra: Totally. I think that’s, that’s the key here. And the balance is something that one needs to identify based on their situation, project, how much of things they need to take care of in their teams. So totally agree to it. Uh, moving on. Uh, I think the most burning piece, uh, I think everyone is talking about it, which is AI, Agent AI, implementing, uh, AI into the most core legacy services in, in a team, in a company. But I think things need to be highlighted, uh, in a way where people need to understand what needs to be done, why it needs to be done, and, uh, while we were talking a few days back, uh, you mentioned about mentoring a few startups and technical founders who are actually doing it at this point of time, and you’re guiding them and you have seen certain patterns where you feel that there is a guidance required in the industry now.
Suresh Bysani: Yeah.
Kovid Batra: So while we have you here, my next question to you is like, what should an engineering manager do in this age of GenAI to, let’s say, stay technically equipped and take the right decisions moving forward?
Suresh Bysani: Yeah. I, I’ll start with this. The first thing is upskilling, right? As we were talking about in our previous, uh, question, uh, most engineering managers have not coded in the GenAI era, right? Because it’s just started.
Kovid Batra: Yeah.
Suresh Bysani: So, but all the new ideas or the new projects, uh, there is a GenAI or an AI flavor to it. That’s where the world is moving towards. I mean, uh, let’s be honest, right? If we don’t upskill ourselves in AI right now, we will be termed legacy. So when there is bottom up innovation happening within the team, how is the engineering manager supposed to, you know, uh, technically calibrate the project/design/code that is happening in the team? So that is why I say there is a need for upskilling. At Eightfold, uh, what we did is one of our leader, uh, he said, uh, all the engineering managers, let’s not do anything for a week. Let’s create something with GenAI that is useful for the company and all of you code it, right? I really loved the idea because periodically engineering managers are supposed to step back like this, whether it is in the form of hackathons or ideas or whatever it is, right? They should get their hands dirty in this new tech to get some perspective. And once I did that, it gave me a totally new perspective and I started seeing every idea with this new lens of GenAI, right? And I started asking fundamental questions like why can’t we write an agent to do this? Why can’t we do this? Should we spend a lot of time writing business logic for this, right? That is important for every engineering leader. How do you periodically step back and get your hands dirty and go to the roots? Sometimes it’s not easy because of the commitments that you have. So you have to spend your weekends or, you know, or after time to go read about some of this, read some papers, write some code, or it could, it doesn’t have to be something outside. It can be, you know, uh, part of your projects too. Go pick up like five to 10% of your code in one of the projects. Get your hands dirty. So you’ll start being relevant and the amount of confidence that you will get will automatically improve. And the kind of questions that you’ll start asking for your, you know, uh, immediate reportees will also change and they will start seeing this too. They’ll start feeling that my leader is reasonably technical and I can go and talk to him about anything. So this aspect is very, very important.
Now coming to your second question which is, uh, what are the common mistakes people are doing with this, you know, GenAI or this advancements of technologies? See, um, GenAI is great in terms of, you know, um, writing a lot of code on behalf of an engineer, right? Writing a lot of monotonic code on behalf of an engineer. But it is an evolving technology. It’ll have limitations. The fundamental mistake that I’m seeing a lot of people are making is they’re assuming that GenAI or the LLMs can replace a lot of strong engineers; maybe in the future, but that’s not the case right now. They’re great for prototyping. They’re great for writing agents. They’re great for, you know, automating some routine mundane tasks, right, and make your product agentic too. That’s all great. They’re moving with great velocity. But the thing is, there’s a lot of difference, uh, between showing this initial prototype and productionizing this. Let’s face it, enterprise customers have a very high bar. They don’t want, you know, something that breaks at scalability or reliability in production, right? Which means while LLM and Agentic worlds offer a lot of fancy ways of doing things, you still need solid engineering design practices around all of this to make sure that your product does not break in production. So that is where I spend a lot of time advising these new founders or, you know, people in large companies who are trying to adopt AI into their SDLC, that this is not going to be a, you know, magical replacement for everything that you guys are doing. It is, think of it as a friend who is going to assist you or you know, improve your productivity by 10x, but everything around a solid engineering design or an organization, it’s not a replacement for that or at least not yet.
Kovid Batra: Makes sense. I think I’d like to deep dive a little bit more on this piece itself, where if you could give us some examples of how, so first of all, where you have seen these problems occurring, like people just going out and implementing AI or agents, uh, without even thinking whether it is gonna make some sense or not, and if you need to do it in the right way..
Suresh Bysani: Yeah.
Kovid Batra: Can you give us some examples? Like, okay, if this is a case, this is how one should proceed step by step. And I think I, I don’t mind if you get a little more technical here explaining what exactly needs to be done.
Suresh Bysani: Yeah. So let’s take a very basic product, right, which, uh, any SaaS application which has all the layers from infrastructure to authentication to product, to, you know, some workflow that SaaS application is supposed to do. So in the non-agentic/AI world, we are all familiar with how to do this, right? We probably do some microservices, we deploy them in Kubernetes or any other compute infrastructure that people are comfortable with. And you know, we write tons and tons of business logic saying, if this is the request, do this. If this is the request, do this. That’s, that is the programming style we are used to, and that’s still very popular. In the world of agents, agents can be thought of, you know, uh, an LLM abstraction where instead of writing a lot of business logic yourself, you have a set of tools that you author, typically the functions or utils that you call, you, you have in your microservices. And agents kind of decide what are the right set of tools to execute in order to get things done. The claim is there’s a lot of time people spend in writing business logic and not the utils itself. So you write this utils/tools one time and let agents do the business logic. That’s okay. That’s a very beautiful claim, right? But where it’ll fail is if I, if you think about enterprise customers, yes, we’ll talk about consumer applications, but let’s talk about enterprise because that’s where most of the immediate money is, right? Enterprise customers allow determinism. So for example, let’s take an application like Jira or you know, Asana, or whatever application you want to think about, right? They expect a lot of determinism. So let’s say you move a Jira ticket from ‘in-progress’ to say ‘completed’, I mean, I, I’m taking Jira as an example because this is a common enterprise product everybody is familiar with, so they expect it to work deterministically. Agents, as we know, are just wrappers around LLM and they are still hallucinating models, right? Uh, so, determinism is a question mark, right? Yes, we, we, there are a lot of techniques and tools people are using to improve the determinism factor, but if the determinism is a 100%, it’s as good as AI can do everything, right? It’s never going to be the case. So we have to carefully pick and choose the parts of the product, which are okay to be non-deterministic. We’ll talk about what they can be. And we have, we obviously know the parts of the product which cannot be non-deterministic. For example, all the permission boundaries, right? One of the common mistakes I see early startups making is they just code permission boundaries with agents. So let’s say given a logged in user, what are the permissions this person is supposed to have? We can’t let agents guess that. It has to be deterministic because what if there is a mistake and you start seeing your boss’s salary, right? It’s not acceptable. Uh, so similarly, permission boundaries, authentications, authorizations, any, anything in this layer, definitely no agents. Uh, anything that has a strong deterministic workflow requirements, basically moving the state missions and moving from one state to another in a very deterministic way, definitely no agents, but there’s, a lot of parts of the product where we can get away with not having deterministic code. It’s okay to take one path versus the other, for example, you know, uh, uh, how, how do I, how do I say it? Uh, let’s say you have an agent, you know, which is trying to, uh, act as a, as a, um, as a, as a persona, let me put it that way. So one of the common example I can take is, let’s say you are trying to use Jira, uh, and somebody’s trying to generate some reports with Jira, right? So think of it as offline reporting. So whether you do report number 1, 2, 3, or whether you do report number 3, 2, 1 in different order, it’s okay. Nobody’s going to, you know, uh, nobody’s going to make a big deal about it. So you get the idea, right? So anywhere there is acceptability in terms of non-determinism, it’s okay to code agents, so that you will reduce on the time you’re spending on the business logic. But any, anywhere you need determinism, you definitely have to have solid code which obeys, you know, the rules of determinism.
Kovid Batra: Yeah, totally. I think that’s a very good example to explain where things can be implemented and where you need to be a little cautious. I think one more thing that comes to my mind is that every time you’re implementing something, uh, talking in terms of AI, uh, you also need to show the results.
Suresh Bysani: Yeah.
Kovid Batra: Right? Let’s say if I implement GitHub Copilot in my team, I need to make sure, uh, the coding standards are improving, or at least the speed of writing the code is improving. There are lesser performance issues. There are lesser, let’s say, vulnerability or security issues. So similarly, I think, uh, at Eightfold or at any other startup where you are an advisor, do you see these implementations happening and people consciously measuring whether, uh, things are improving or not, or they’re just going by, uh, the thing that, okay, if it’s the age of AI, let’s implement and do it, and everything is all positive? They’re not looking at results. They’re not measuring. And if they are, how are they measuring? Can you again, give an example and help us understand?
Suresh Bysani: Yeah. So I think I’ve seen both styles. Uh, the answer to this largely relies on the, you know, influence of, uh, founders and technical leaders within the team. For example, Eightfold is an AI company. Most of the leaders at Eightfold are from strong AI backgrounds. So even before GenAI, they knew how to evaluate a, a model and how to make sure that AI is doing its job. So that goodness will continue even in the GenAI world, right? Typically people do this with Evals frameworks, right? They log everything that is done by AI. And, you know, they kind of understand if, uh, what percentage of it is accurate, right? I mean, they can start with something simple and we can take it all the way fancy. But yes, there are many companies where founders or technical leaders have not worked or they don’t understand AI a lot, right? I mean, there’s, they’re still upskilling, just like all of us.
Kovid Batra: Yeah.
Suresh Bysani: And they don’t know how to really evaluate how good of a job AI is doing. Right? I mean, they are just checking their box saying that yes, I have agents. Yes, I’m using AI. Yes, I’m using LLMs, and whatnot, right? So that’s where the danger is. And, and that’s where I spend a lot of time advising them that you should have a solid framework around observability to understand, you know, how much of these decisions are accurate. You know, what part, how much of your productivity is getting a boost, right? Uh, totally. Right. I think people are now upskilling. That’s where I spend a lot of time educating these new age founders, especially the ones who do not have the AI background, uh, to help them understand that you need to have strong Evals frameworks to understand accuracy and use of this AI for everything that you are, that you’re doing. And, and I see a huge, you know, improvement in, in, in their understanding over time.
Kovid Batra: Perfect. Anything specific that you would like to mention here in terms of your evaluation frameworks for AI, uh, that could really help the larger audience to maybe approach things fundamentally?
Suresh Bysani: Oh, I mean, so there are tons of Evals frameworks on the internet, right? I mean, pick a basic one. Nothing fancy. Especially, I mean, obviously it depends on the size of your project and the impact of your AI model. Things can change significantly. But for most of the agents that people are developing in-house, pick a very simple Evals framework. I mean, if you, I, I see a lot of people are using LangGraph and LangSmith nowadays, right? I mean, I’m not married to a framework. People can, are free to use any framework, but. LangSmith is a good example of what observability in the GenAI world should look like, right? So they’ll, they’re, they’re nicely logging all the conversations that we are having with, with LLM, and you can start looking at the impact of each of these conversations. And over time, you will start understanding whether to tweak your prompt or start providing more context or, you know, maybe build a RAG around it. The whole idea is to understand your interactions with AI because these are all headless agents, right? These are not GPT-like conversations where a user is trying to enter this conversation. Your product is doing this on behalf of you, so which means you are not actually seeing what is happening in terms of interactions with this LLM. So having these Evals frameworks will, you know, kind of nicely log everything that we are doing with LLM and we can start observing what to do in order to improve the accuracy and, you know, get, get better results. That’s, that’s the first idea. So I, I would, I would start with LangSmith and people can get a lot of ideas from LangSmith, and yes, we can go all fancy from there.
Kovid Batra: Great. I think before we, uh, uh, complete this discussion and, uh, say goodbye to you, I think one important thing that comes to my mind is that implementing AI in any tech organization, there could be various areas, various dimensions where you can take it to, but anything that you think is kind of proven already where people should invest, engineering managers should invest, like blindly, okay, this is something that we can pick and like, see the impact and improve the overall engineering efficiency?
Suresh Bysani: Yes. I, I generally recommend people to start with internal productivity because it is not customer-facing AI. So you’re okay to do experiments and fail, and it’ll give a nice headway for people within the company to upskill for Agentic worlds. There are tons of problems, right? Whether it is, I mean, I have a simple goal. 10% of my PRs that are generated within the company should be AI-generated. It looks like a very big number, but if you think about it, you can, all the unit tests can be written by AI, all the, you know, uh, PagerDuty problems can be, can, can be taken at first shot by agents and write simple PRs, right? There are tons of internal things that we can just do with agents. Now, agents are becoming very good at code writing and, you know, code generation. Obviously there are still limitations, but for simple things like unit test, bugs, failures, agents can definitely take a first shot at it. That’s one. And second thing is if we think about all these retro documents, internal confluence documents, or bunch of non-productive things that a lot of engineering people do, right? Uh, agents can do it without getting any boredom, right? I mean, think about it. You don’t need to pay any salaries for agents, right? They can continuously work for you. They’ll automate and do all the repetitive and mundane tasks. But in this process, as we’re talking about it, we should start learning the several frameworks and improve the accuracy of these internal agents, and thereby, because internal agents are easy to measure, right? 10% of my PRs. My bugs have reduced this much by a month or 1 month. Bugs as in, the overall bugs will not reduce. The number of bugs that a developer had to fix versus an agent had to fix, that will reduce, uh, over time, right? So these are very simple metrics to measure and learn, and improve on the agent’s accuracy. Once you have this solid understanding, engineers are the best people. They have fantastic product context, so they will start looking at gaps. Oh, I can put an agent here. I can put an agent here. Maybe I can do an agent for this part in the product. That’s the natural evolution I recommend people. I don’t recommend people to start agents in the product direction.
Kovid Batra: Makes sense. Great. I think Suresh, this was a really interesting session. We got some very practical advice around implementing AI and avoiding the pitfalls. Uh, anything else that you would like to say to our audience, uh, as parting advice?
Suresh Bysani: Yeah, so, uh, I’m sure there is a lot of technical audience that are going to see this. Uh, upskill yourself in agents or AI in general. Uh, I think five years ago it was probably not seen as a requirement, uh, with, there was a group of people who were doing AI and generating models, and majority of the world was just doing backend/full stack engineering. But right now, the definition of a full stack engineer has changed completely. Right? So a full stack engineer is now writing agents, right? So it doesn’t have to be fine tuning your models or going into the depth of models, right? That is still models experts’ job, you know? Uh, but at least learning to write programs using agents and incorporating agents as a first class citizen in your projects; definitely spend a lot of time on that.
Kovid Batra: Great. Thank you so much. That’s our time for today. Pleasure having you.
Suresh Bysani: Thank you. Bye-bye.
Are your engineering metrics actually driving impact? 🚀
Many teams struggle with slow cycle times, high work-in-progress, and unclear efficiency benchmarks. The right DORA metrics can change that — but only when used effectively.
In this session of ‘The Hows & Whats of DORA’ webinar powered by Typo, host Kovid Batra is joined by:
🎙️ Mario Viktorov Mechoulam — Senior Engineering Manager at Contentsquare & metrics expert
🎙️ Kshitij Mohan — Co-Founder & CEO of Typo
Together, they break down the science of engineering metrics, how they’ve evolved, and their direct impact on team performance, shipping velocity, and business outcomes.
✅ Why DORA Metrics Matter — The role of cycle time, deployment frequency & work in progress
✅ Optimizing Engineering Efficiency — How to balance speed, stability & delivery quality
✅ Avoiding Common Pitfalls — The biggest mistakes teams make with metrics
✅ Connecting Metrics to Business Outcomes — How they influence revenue & customer satisfaction
✅ Getting Leadership Buy-In — Strategies to align executives on the value of engineering metrics
✅ Live Q&A — Addressing audience questions on industry benchmarks, emerging trends & best practices
Kovid Batra: Hi everyone. Thanks for joining in for the DORA Metrics Webinar ‘The Hows and Whats of DORA’, powered by Typo. This is Kovid, your host, and with me today we have two amazing speakers who are the front runners, uh, the promoters of bringing data-driven engineering to the engineering world and making dev teams even more impactful. Please welcome the metrics expert tonight, Mario.
Mario Viktorov Mechoulam: Hello. Hello everybody. I’m very happy to be here again, uh, with you Kovid, and meeting you, Mohan.
Kshitij Mohan: Yup. Same, same here, Mario. Glad to be here, uh, with you and Kovid. So yeah, thanks. Thanks for joining in.
Kovid Batra: Thank you, Mario. Thank you for joining in. And the second guy on the screen, uh, he’s the co-founder and CEO of Typo, Kshitij Mohan. Uh, I’ll be talking more about Kshitij in the coming few seconds and minutes, but, uh, before we get started, I’d like to tell you something. Uh, so my idea of bringing these two people, uh, on this, on this, uh, episode, on this session was to basically bring two of the best guys who I have known in the past few years, uh, Mario and I met recently, but both of them, uh, have very deep knowledge, very deep understanding of how the engineering world works, how engineering metrics particularly work. Uh, Mario has been there to help us out in the last one month for any kind of webinar or podcast and then, he has been there in the industry from the last 15 years, uh, a true metrics enthusiast working with his team at ContentSquare, implementing those metrics, tackling things at the ground level, all the hurdles. And with Kshitij, uh, uh, we, we have, uh, spent almost, uh, three to four years now, uh, uh, and I’ve seen him doing the hard work building Typo, uh, talking to multiple engineering teams, understanding their problems, shaping Typo to where it is today, and, uh, honestly, helping and enabling those teams to, to get more success. So with that, I introduce both of my guests, Mario, and Kshitij to the, to the show. Uh, welcome to the show once again, guys.
Kshitij Mohan: Thank you. Thank you. Thank you so much, Kovid. I think you’ve already said too much about us, so really humbled to, to hear what you are, what you have been saying. But yeah, thanks. Thanks for having us here, man. Yeah.
Kovid Batra: Great guys. So I think, um, Mario, uh, you are going to be, uh, our, our key speaker today, uh, with, of course, but we are looking at a lot of learnings today coming from your end. But before we jump onto that section, I think, uh, I would love to know something about you, the audience would love to know something about you. Uh, tell us something about your hobbies or people, uh, who inspire you, uh, whom you look up to, and what, what exactly what trait in them, what quality in them inspires you.
Mario Viktorov Mechoulam: Right. Uh, I have to go here with my wife, not because she’s listening, uh, but because I’ve never seen anybody so determined and so hardworking. I think for her, nothing is impossible and, uh, I, yeah, I can’t stop admiring her since the day we met and I want to be more like her.
Kovid Batra: Perfect. Perfect.
Kshitij Mohan: You already chose the best answer, Mario. I don’t think we have anything else left to say.
Kovid Batra: Uh, same question to you, uh, Kshitij, uh, who, uh, who inspires you? What quality in them inspire you?
Kshitij Mohan: Sure. I think other than my wife, right? So who is definitely the most hardworking person, the, the bread earner of our house, because you know, how a startup work functions, it runs. So I think I deeply admire Roger Federer, so he’s one of my, my heroes, core to my heart. So I, I am, I’m a, like, deep, uh, sports enthusiast and definitely Federer has been a symbol for me of hard work, persistence, of flawlessness, uh, the, the elegance that he brought to the game. I think this is all what I have been trying to learn and, and implicate all those learnings into what we are building today. So, yeah, I would say that’s, that’s something that I really admire about him.
Kovid Batra: Interesting. No, a really interesting choice. Even I admire him a lot. And, uh, um, the hard work that, I mean, the, the game he has, I think I, I love him more, more for, for that actually. But yeah. Great, great inspiration for you. Uh, perfect, guys. Thank you so much. Uh, I think we jump onto the main section now where we talk about engineering metrics, DORA, beyond, like implementing them, the challenges that you have seen implementing them in the teams. So we’ll start with the first, very basic question. Uh, and the question to you, uh, is, is to you, Mario, uh, why do you think these metrics are important? Of course, you are an enthusiast. You, you do know how it is, uh, to have a team with or without metrics. So why do you think these metrics are important? Why these teams should implement it, and how things are evolving in this space? Like we are talking about DORA, but things have moved beyond it because DORA alone would not be sufficient to suffice, uh, measuring the, uh, engineering efficiency or productivity. So what are your thoughts on that?
Mario Viktorov Mechoulam: Absolutely. Thanks for the question. I think they’re important because, uh, they can be a stepping stone to, to doing better. Um, we are, we are looking into business impact and business results, and I think we can all agree that there might be some metrics that are good proxy indicators on what can go well or what can be improved. So if, if we agree on that, I think we can also agree that it’s smart to measure them. Once we measure them, we, we become accountable of them. So, summarizing a bit, uh, all this loop, um, we need to make space for the team so that they can, um, discuss and analyze what is going on. We need to make sure that there is, if there is any knowledge gap, this knowledge gap is covered so that we can, um, we, we can really, uh, understand what impact there could be in changing something that we have. And of course, we have to prevent the metrics of being misused because otherwise we lose all the trust. And in the end, what we want, want to, to make sure is that we, we have quality data that we, we can base our decisions on. So if we manage to do that, um, that’s already, uh, a big milestone. I think DORA, DORA is great. DORA is one of the best things that we have in the industry. It’s, uh, if I’m not mistaken, the only peer reviewed, um, metrics that we have that has, that they have stood the test of time and scrutiny, which is, which is good. So by all means, um, if you don’t know where to start, DORA, DORA can be a good place. Um, now I think that as with any metrics, just, uh, using. DORA as a target can be, uh, can be, uh, can be wrong. Um, also having, you using specific metric values as a target can also be wrong. Um, so if, if I look a bit, uh, broader, what do I see? I see that many teams nowadays are no longer caught up in these, um, old-fashioned ways, uh, ways of, of doing things. Uh, we, we tried to bring manufacturing, uh, into engineering, and, and that went, uh, very wrong. We, we all remember the, the Gantt charts, uh, multi, multi-year programs that never met the deadlines, these type of things. So to, today, if you want to start with DORA, uh, you might often find that your team is already high-performant. What do I mean with that? If we talk about, uh, lead time for change, uh, if we know that we can already ship in a few hours up to a few days. Uh, if I look at, um, um, the deployment frequency, we know we can ship as many times per day as we need. Uh, we are probably already high performing teams, so what do we do for there? Uh, I think we have to, um, we, we have to look further. And, um, one of the, uh, one of the ways to do that is to, to, to stop putting the focus only on engineering, right? So, um, DORA might be great if you want to, if you already feel that you have a big issue inside your, your coding or your pipeline, uh, areas. Uh, they can also be great if you want to optimize that final 2–5%, but they often cannot change a lot in the grand scheme of things. Right? When we talk about engineering, we are often talking about 10–30% time of the lead time since the moment we a customer requests something, or the moment we, we have an idea that we have to deliver, uh, we want to deliver to our customers, and if we, from this 10–30%, we want to narrow it down further to just the coding part, that’s maybe 10% or less per the 10% of the, of the total time. So we, we are missing a lot. And also by doing that, we are sending a message that this is just engineering. This is not product, this is not design, this is focused on just the team or not looking broader. So what I would do is start expanding with all the metrics. We have customer metrics, we have well-being metrics, we have cycle time, lead time metrics that start taking into consideration much more. We have operational metrics that, uh, for example, um, can, uh, can help us identify how frustrated the customer is. So these, these type of things.
Kovid Batra: Makes sense. I mean, uh, a lot of times I have also seen while talking to the clients while understanding what they’re exactly looking for in these metrics, and I found out they, they started with DORA. They, they were good with cycle time. They were good with deployment frequency to look at and the first go. But as they evolved, they started looking at the bigger picture. As you said, they wanted more metrics, more visibility, not just metrics, but more visibility that DORA was not able to provide. So, I mean, uh, if, if I have to ask you like, at, at, uh, ContentSquare, or, or companies where you have worked previously, how, how did you start and, uh, how it evolved? Can you just share one example so that we, we can more relate to how things evolve when someone starts with DORA and then move to some other metrics that help them help understand a better picture?
Mario Viktorov Mechoulam: Of course. I have to confess that I have never started with DORA. Um, I think that, um, that can be a great place to start if you, if you don’t know where to start. But, uh, if you already are aiming high, then there’s no reason to start with that. So to me, there are two metrics that if we look at, uh, the efficiency, uh, domain of things. So doing the things right, um, might, might be key, key metrics. So one of them is cycle time. So from the moment we start working on something until the moment it, we put it in the customer hands. So this would include not only, um, co, um, uh, pull request, review time and deployment time. We’re including the coding time, we’re including the design time, the conception time, these type of things. Uh, the other one, and, and this is the lagging indicator, right? So by the time we have some actionable, uh, actionable data on this metric, it might be already too late, but we, we, we are still on time to make changes in the future. So I, I, we can talk about that in, in detail. This is one of the, I think, most important things that the team should, should, uh, sit, sit down and do together. The other, which is not a lagging indicator, is work in progress. There is no better indicator for you to realize if you’re being efficient and focused than to look at work in progress. Uh, I mentioned it the last time, but if you have 30 work, uh, 30 things ongoing with a team of five, there is something deeply wrong in, in the way you, you are working with your team.
Kovid Batra: Great. I think that that’s a good example. Great.
Kshitij Mohan: Sorry. Sorry. That’s a great point, Mario. And I think this is where I would like to add few things and talk more around that front as well, right. So, uh, what we have seen personally across our journey as well, right. So, uh, the first question that always comes is that why do we need metrics? So that has been, I think, the first starting point for each and every engineering leader today, which is pretty surprising to us, because like at a fundamental way, as you mentioned, right? So engineering should be run with clarity and not with chaos. I think this is where the core mission statement is what we started off with. And every other critical function in any organization has some indicators, has some metrics, has some goals to follow on which they are tracking and they are moving somewhere or the other, and, and tech being such a, a, I think, uh, so much effort, so much cost, everything being put in, this is really surprising one why still we need to think on, hey, whether should be there any core set of metrics system or not, because there should be. However, I think the biggest challenge that always comes across is, and this is what we have heard most of the folks say at a very first upfront way is that, hey, uh, metrics could be gamed, right? And I think this is what we have been constantly trying to solve in some way or the other end, like trying to figure out, say, do all those discussions that hey, we know, right? So yes, if you put out very, uh, I would say rudimentary metrics, and if you start making people accountable for those metrics on a one-on-one way, then yes, they’re gonna not feel driven by it, but more around by, pushed by them, and that’s where the real, like the gaming of the system starts happening. So hence it also becomes really important that how do you design and define your metrics. I think this is what I think I would love to talk more around and would hear, love to hear your experiences as well that you, okay, there’s a first thought that yes, let’s identify or let’s go and implement this metric system in place, but right, how do you go and decide and define what could be those right areas, uh, we should be starting off with? So I think if you have any thoughts or if you have done that some way or the other, I think that would be really great for everyone to kind of just know what those starting points could look like.
Mario Viktorov Mechoulam: Right. Um, first of all, I think, I think you’re right. Um, there is always the risk that metrics, uh, get gamed. Yeah. Um, that’s why the metric itself should not be the target, nor, nor the value the metric should be additional data that we can use to, to make better decisions. I have, I have evolved what I’ve, I, I do over, over the years, and I don’t think that what I do today is, is perfect. Far, far from it. I think, uh, it’ll continue to evolve a lot, uh, in the, in the coming years. But I think that at least we need, um, two, two main areas or two main domains when we design our metrics. Um, and by the way, one way is to do them alone, to invite the team to do something, but the other is to, uh, to make them part of this, of this decision and this discussion. Sometimes this is easier, sometimes you have more time to do it. Other times, uh, the team asks you, just give me a jumpstart so you know about it. So recommend me something and then we can move from there on. So the two main areas that I think should exist are, um, areas targeting efficiency. So doing the things right. And areas targeting, targeting, uh, effectiveness. So doing the right things, right. We, because we want to be correlating the, the output, uh, of what we do with the, with the impact that we want to see, right? Yeah. And then, uh, be able to adopt there. Uh, with time, I, I realized how important it’s also to, to, um, to have more, um, self-reported metrics, well-being metrics. So two that I think are staple and I think some of the frameworks like SPACE I recommend, um, are, are the well-being and, and the satisfaction of the work you do. And, and the other, for me, it’s very important is the focus time. So in the end, um, I, I don’t think this will come as a surprise to anybody. There was a, a new paper published, I think by, by, uh, one of, well, at least one of the outsources, but the same, uh, for DORA, Nicole, um, I forgot what’s her name.
Kovid Batra: Nicole Forsgren.
Mario Viktorov Mechoulam: Yeah. Um, and, and, and answer, unsurprisingly, they, even though it was a small one, they saw a correlation between, uh, time available to do coding and work being delivered faster and with less defects. Uh, now that study, I, I mean, I, I’ve not, uh, read it, uh, all through yet, but, uh, it was focusing on individual contributors. I think it’s much more important, uh, to focus on teams. Uh, teams are already a local, optimal, right? So let’s not make that even, even more local, uh, with another one. So I think it’s important to, to measure this as a, at a team level. And, and finally, I think we should not forget, especially with the advent of DevOps, that, uh, you own what you ship, and it’s really important that your, your ownership and accountability doesn’t end when you put something in production, right? So I, I like to, to here to track, um, how many defects, uh, end up appearing on production. So this means that potentially there is quality gates that are not good enough, and we might then decide, uh, does it make sense to do a further investment based on the defects that end up in production? But there is only one thing that’s worse than having defects in production, and that is the customer discovering these defects before you. Right? So, if this happens, then you should definitely track these, it means that not only your quality gates are faulty or can be improved. It also means that your monitors and alerts, uh, uh, are lacking because the, yeah, you want to, uh, you want to prevent this frustration.
Kovid Batra: True, I think, uh, in some great insights here, but, uh. Just mentioned is one of the problems that we see. Uh, and then circling back to the hurdles that teams, uh, actually face while implementing these metrics. So gaming of these metrics is one of the problems, but along with that, there comes many more challenges, right? And I think, uh, these are the next things that we want to discuss with you, Mario, uh, with your experience. I think, uh, first thing is people hesitate to start, right? Like even take up these initiatives and think of getting a buy-in from, let’s say the executives, uh, there is push from, uh, peer teams whether it is required or not. So there are many such reasons why people don’t even start. So how do you recommend exactly this initiative should start within teams? Because it’s not about how to implement certain metrics for certain use cases. Of course, that is the next layer of problem that we will need to solve when these metrics are getting implemented. But before that, there is, uh, uh, a lot more to be known on how to take up these initiatives, how these initiatives shape up in a team in the best way possible. So I’m sure you have experiences on that as well. Uh, would you like to throw some light and tell us how, how this works out and how one should start?
Mario Viktorov Mechoulam: Yes. With, with pleasure. Um, so the, the best moment to start is now. Not now. After, after the webinar, of course. There you want, you won’t have find the perfect moment in which, uh, your team will align with the way, uh, that you think your executive will align, uh, with the way you think. That, that’s not gonna happen, magical new budget is not gonna appear. So the best thing that you can do is start right away. There is also free material that can be very helpful. One, one, one book that I always recommend is the ‘Flow Metrics for Scrum Teams’. It’s a free book from the ProKanban organization. I think that that’s great. At least, at least the first chapters, um, help you understand the concept of flow. And after that you have to, uh, you have to crunch some of the metrics. Um, unfortunately, most, most of the popular software that we have, uh, we, we use, like Jara is notoriously bad for anything that, that is not, uh, scrum. So it might be a bit complex. You can also do this manually, but I advise against that. So that leaves us with, uh, the option of, uh, choosing a tool, choosing a tool. Uh, we are lucky to live in a, in a moment where, um, there is, uh, a competing market. So there is, there is plenty of tools to choose from and many of those tools also are nice enough to offer free trials and to onboard you and your teams. So why is this important? Because it is much more important to, um, to get, uh, support from, from, from execs and support from your team if you already have some data to show from it. Yeah. Uh, don’t get me wrong, there is one part which is you still have to sell it and sometimes you have to, uh, maybe over-promise and sometimes you have to, um, shorten all these, all these links and notes that go between starting to implement metric, implement metrics and seeing some results. Sometimes you have to say, oh, we’ll, we move faster, right? Or we’ll be more predictable. That’s not really the end goal. That might be a byproduct. Uh, the end goal is to, to change a bit the culture, to change the way people think to, um, to, to help them see data in the same way they see code reviews or pair programming or test-driven design or, uh, CI uh, CD pipeline, these type of things.
Kovid Batra: Perfect. I think, um, one thing you just mentioned about, um, around the adoption part where you go out, try something, do a POC, and then with that POC, you, you go back to the teams and executives saying what exactly worked out, what could, what could not exactly work out with these kind of tools. So when, when somebody’s into that situation, uh, how do you exactly recommend that one should go about, uh, picking up certain metrics? Can we take an example here and, uh, maybe from your experience that how exactly one should go about picking few metrics, working on those, showing those numbers to the executives, and then moving on to the next step for the wider teams and, uh, the whole organization?
Mario Viktorov Mechoulam: Yes, engineering organization. Of course.
Kshitij Mohan: Also, I think just, just to add one more thing here. So, uh, what we have realized is, and I I’m, I’m pretty sure you might have some experiences around that front as well, that, uh, it’s never possible, uh, to show any change, let’s say in the first 14 days or in within a month or, or, or one and a half months or so, right? So that’s, and the challenge that, hey, so what we usually recommend is that, hey, you need to spend actively, at least the first three months where you build the processes, start, identify, measure, execute, right? But that also becomes one of the core challenging aspects because firstly, if there are no dedicated folks who are working on that front, and then they start slacking. And then the other part is if there are no results, then everything has to be blamed on the, on whatever methodology or whatever the tool that that team is incorporating. So how do you balance that front as well?
Mario Viktorov Mechoulam: Definitely, yeah. Lots of very good points. Uh. So, so yes, you’ll end up having to spend some of your own money possibly, and that is a good thing. It means that you believe in it and it’ll give you more time to, to, to show some results. Uh, second, very important, not to mention any mythology with capital M, any, any framework so that people don’t link that, uh, to something that they can blame afterwards. Yeah, link it to, to outcomes and to information that they can use. Um, and finally, it, it is true that, um, it might be hard to see results quickly. That is why initially I recommend not to go all in and try, uh, 10, 20, 30 metrics. Go with one or two and, and, and one or two, that that can be combined and have, um, have synergies with each other. And so for me, that would definitely be the cycle time and the work in progress. Um, so I, I can tell you what we do, uh, what do we like, what, what do I like doing with cycle time. Um, I, I like to track from start to end, and I think this is a staple for anything because you cannot apply it at team level. But the beauty of it is that then you can apply it at, at group level, and then you can apply that at line level and you can apply it at organizational level. And in each of these flight levels, you can measure something different. For a team, maybe it’s important tasks because you’re seeing how, how, how quickly can we get into flow with the right priorities and sequencing. But then at line level, line level, maybe it’s important because you have a strategic initiative for the year that you really want to see delivered, and that helps you see when it is moving and when it is being blocked, because maybe not all teams are aligned around the same OKRs. So with, with cycle time, what I like doing is every, every two weeks or so, we sit together and we identify everything that has been shipped in this period, and we, and we check the cycle time. And, and you can see, what you can see is the, the, um, bell shape, shape distribution, right? So not, not every, uh, task stays the same time. You, you go from a few hours, up to a few days, maybe a week, sometimes two weeks, or there is more complicated work that sometimes, uh, because of, of its nature, of its, uh, conditions can take even longer. Um, and then this bell-shaped Q curve can also be binomial. So it could have two heads or more heads. Uh, it could have a long, long tail distribution. If you see something uniform, you’re doing it wrong, you’re definitely doing something wrong, right? Uh, but what, where I’m going with this is that in the end, this gives you a spectrum of probability. You know that after a certain time, um, unless, unless the nature of the work that you have to do changes drastically, and that’s unlikely for teams, except for the yearly re-org maybe that, uh, we all do.
Kovid Batra: Yeah, yeah.
Mario Viktorov Mechoulam: Um, or except if the team’s composition changes drastically, this same probability distribution is gonna be maintained. And, and, and after two, three weeks, you already, we will have some degree of confidence of this, of this being maintained. Um, and, and that means that you can use that maybe to forecast a bit the future, to understand what is your base of delivery, what, what’s your attack time. Okay, but what, what do we do? So maybe we see something which is binomial. It has a long, uh, a thin tail distribution. We want to be better. Um, so to be better, what we have to do is we have to narrow this bell, bell shape, right? So we have to reduce the standard deviation, and we have to understand what are the things that occur that are not adding value, that are waste; waiting time, handover, et cetera. So what, what I like doing is we, we look at, at the period and we grab the outliers. So the thing is that had the, the highest cycle time, and we analyze them as a team. No blaming, but we have to go through the events and conditions that made this thing take for example, four weeks or five weeks or whatever. And oftentimes, we have the same culprits. So we are not, uh, we’re not gonna publish a paper by, by doing this. We’re not doing rocket science. You have, um, the slices of work are much larger than what they should. You have scope creep. Uh, you have ill definition. So you had to do something, but it was not clear, we did something else. You have defects, you have waiting time, you have handovers. You have so many things. Uh, you have process issues, you have infrastructure issues, uh, so, so many things that, uh, they’re always the same. So when you analyze two or three of those, it can take you all from half an hour to an hour. But this is, uh, this is the quality, qualitative analysis that is, that is useful. You can already identify some patterns. For me, uh, at one point it was the pipeline was not automated. We could not release every day. Um, some of the times it was, there is a lot of dependencies on this work with other teams. We didn’t identify them earlier. Um, we, you identify these patterns and then this offers you ground to take actions, right? So as a team, you can agree, say, oh, this happened. We know what, what causes it, what actions do we take to prevent that? And over time, you’ll see that the goal is that this, these outliers in the cycle time, they disappear because if they disappear or move or have a lower cycle time, not only, uh, your average, uh, cycle time looks better, but the, the whole standard deviation gets narrower and narrower. Yeah. So at some point, if you can say, oh, my quantile 85 or 95 is below a week, or around a week or below two weeks, that’s already great. And people can decide to gain that or not gain that. But that gives you already an idea of how good are we at sequencing and having a, a cadence of delivery.
Kovid Batra: Yeah, makes sense. Totally makes sense. Very good example there. But, uh, I, I have, uh, one, one doubt here. Uh, but, uh, Kshitij, do you have something to add here or do you have a question?
Kshitij Mohan: No, no, I think that’s fair enough. Uh, but I think one more, uh, just a point to it. So there is also a question, uh, when we are looking at some metrics and maybe could be cycle time or some review times, and also, uh, what some folks have come back and asked us as well that, hey, should we look at the averages, the medians, or should we be looking at, uh, the, the deviations on, on how to act on? So any, any thoughts around that as well, Mario?
Mario Viktorov Mechoulam: Um, I think it depends, but this is one of the lowest hanging fruits in terms of conversation that you have openly with your team. Maybe for which specific metrics should we start with, uh, the team prefers your advice, but this is already something that, uh, but just by looking at the numbers you can have normally for a cycle time, I like taking a look at the 85 quantile because I mean, averages sometimes are too good and we are not doing metrics to tap ourselves on the back or as just a vanity thing, right? So what we, one of the purposes we use the cycle time is to maybe to be able to understand how likely are we to deliver something on time or what’s the cadence, right? So it doesn’t help if we use the quantile, uh, if you use the average, right? Because, um, yeah, so half of the time you’ll, you’ll deliver in, in this amount of time, but then what about the rest? And the rest could go close to the infinite, right? And, and you’re not interested in that. You’re interested in, in doing this. So you can generate some trust with your stakeholders. At some point, when you see, when, when, when, when your stakeholders and, and the exec see that you deliver as long as you’re allowed to put the focus on one thing and have the time, that’s it. Then you, you stop getting the question of when is something gonna be delivered, because they know it’s a matter of letting you do the work.
Kshitij Mohan: Yeah, right.
Kovid Batra: Um, but I think one thing, uh, Kshitij would also be able to relate to this, uh, a lot of teams have come back to us, uh, where they just say that these metrics don’t tell the real picture and we need to understand the context behind it. And you already raised the point around it that the qualitative analysis of these metrics is also very important. So when we say qualitative analysis, in, in my understanding, we are talking about understanding the context around that particular problem in the engineering team for which we are looking at the metrics, and how do we go about it?
Mario Viktorov Mechoulam: Okay. So if I understood the question, um, how do we make sure that when we discuss metrics, we include things that are, um, directly impacting the, the, the metric that we see, right?
Kovid Batra: Yes, yes.
Mario Viktorov Mechoulam: Yes. Uh, this is a bit more complicated. Um, and it’s, uh, so it, it’s unrelated, but you definitely need it. And this is one of the first things that you should do with your teams, and that is generating trust. You, you want to make sure that when you sit together, um, people feel comfortable enough that they tell you the truth without fear of being blamed. Uh, right? So if, if somebody has a perception that this is a problem or this is going wrong, or this is not good, or we lack, lack something, you want to know about it. Yeah. You want, because when you know, when you know about it, you can try to measure it. You can try to do something and see if, uh, the result is moving the needle in the direction that you want to or, or not.
Kovid Batra: Perfect.
Kshitij Mohan: Yeah, I think just to add on to that. So there could be some qualitative data also that could be looked at in a way. So for example, uh, and, and this is something that, that we are also trying to make more robust as well, is that if you can start comparing some qualitative with the quantitative part. So for example, let’s say, if you look at for example PR size of the review time as one of the metrics, right? So that could be definitely one of the ways, uh, where if you are looking at it, then that’s definitely one good indicator. But if you can start correlating it, for example, uh, on the data based directly coming from developer surveys on developer flow. So that becomes a good way to look at a complete picture as well because one way you are trying to look at how exactly at a transactional level, how the pull requests are flowing, for example, and on top of it, let’s say if the developers are coming back and sharing this feedback that, hey, our flow is mostly blocked because of X, Y, Z reasons, then, then this becomes a, a good picture to see at an overall level, hey, this is the real, uh, context that we might need to solve because unless and until we don’t solve this, the review times are always gonna be, uh, spread across, uh, the spectrum. So maybe that could be a few of the ways to add on to the picture as well.
Mario Viktorov Mechoulam: Yeah, definitely.
Kovid Batra: Yeah. I think, uh, to sum up the point, uh, from where we started that what exactly one should take up initially to do that POC and probably go back to the team and the execs to make sure that things are moving and then getting a bigger buy-in for the wider adoption, uh, is looking at one or two important metrics, understanding the quantitative as well as the qualitative part along with it so that it makes complete understanding of what strategies, what action points need to be taken. And then when you have results, you go back to the team and the execs to make sure that, yeah, this is something that we should go ahead with and adopt in the team. So, great guys, I think this, this is one interesting piece that I really wanted to discuss. Uh, other than that, I think a lot of people do it, but they don’t do it the right way. Uh, there are pitfalls to how you, uh, use these engineering metrics or how you implement these engineering metrics. So any examples, uh, that you would like to share, Mario, uh, that you have seen are how not to use the metrics basically?
Mario Viktorov Mechoulam: Uh, yes. Um, yeah, so, so there is three areas that normally you have to take care of when you, when you start using metrics, uh, that is covering the knowledge gap, preventing the metrics from, uh, from being, uh, misused and, um, and right now, I forgot the third one, but, uh, it’ll come up to mind afterwards. Um, one, one of the, one of the key things that we need to do when, uh, we start implementing metrics, uh, is to avoid falling into the pitfall of, uh, keeping everybody busy, 100% utilization. And I understand the context right now is, um, is, is delicate. Um, many companies are looking to optimize the cost to make sure that, um, the, the, the people, the resources are invested in, in what’s best. So this might be translated into are people working on something or are they idle? And, and best is, uh, it’s debatable here. So I, I would like to, to to, um, to start with a question on that, that you can also use for, to ask, uh, to ask your team, uh, which is, I think it comes from Tai Chiana. I could not find the quote, uh, when I looked for it, but, uh, but I think it was him that at least I remember somebody telling me about that. And it is, uh, what, what do you find more bothersome, uh, when you walk into a workplace? Uh, is it, uh, seeing people or resources idle or seeing everybody busy? And if you’re watching this now, you’ll have some time to think. If you’re watching the recording, you can pause and think about the question and think about why. And I, I think most of us, um, even though being ideal sounds, sounds strange, there is a hunch. Our gut feeling is, is is to, to say, no, no, I think seeing everybody busy is, is more bothersome. And, and to help with the answer, I think we have another question, which is, would you go, would you want to go into a hospital that has everybody 100% of the time busy? No, of course. Especially if it’s a life or death situation. Um, so, so keeping, keeping people busy just for the sake of it, um, can be very dangerous and it might slow everything down. Why, why you don’t see it? One, one exercise I like doing with my teams is to try, uh, to, to, to invite them to design what would be, um, what would be the way to determine, uh, our, our system through something that is closer to them, like technology. In this case, I usually use Kafka, uh, producers, consumers, and, and, and the first question I ask them, okay, if you, if you were to design producers and consumers with Kafka in between, uh, would you like that given, you know, what is your, your output, you have an input that is higher than the output, so you’re not able to process in time all the traffic. So what happens then? And naturally, everybody knows that what happens then is that if you have a queue, this queue becomes larger and larger. So the, the service time, uh, tends to influence. Yeah. So, so this is the first lesson. If you, if you have, if you, if you have a system, you want this system to be stable, uh, but you can go further. Um, I don’t think any of us would design, uh, a system with, uh, producers, consumers, and Kafka in between, uh, where you match exactly the input capacity, uh, that you have, right to the, to the processing power. That’s, that’s extremely risky. Yeah, so I think the same thing happens, um, the same thing happens when you, when you work with teams. Uh, you want to have some leave some space for, for maintenance, for downtime, for reflection, for improving, for this type of thing. So that, that’s the second learning. Uh, then we can take it a step further and we can start thinking about where are bottlenecks. So you don’t have a single system, you have multiple, you have a chain of them. Uh, your speed, your delivery, uh, will be as good as the slowest link. That’s right. And, and here we’ve seen this reflected in the teams many time. So who is responsible of the conception, technical design documents? Everybody. Who’s responsible for coding? Everybody. Who is doing pull, pull request review, code review? Everybody. Who’s doing QA? Oh, that poor guy over there. Who’s doing, uh, product validation, UAT? Oh, that, that other person, right? So this is wrong, I think here. Uh, we have to design our system in a way where everybody’s responsible for every, everything, so that we don’t have bottlenecks. There is also the, the forced learning that we can apply through this exercise is, uh, you have to, it’s not about designing the system and living it there. You have to be able to adapt to, to the demands and adjust to demands, right? So sometimes we know there’s gonna be an event and the capacity that we have is not good enough. Um, the, the same thing happened the other way around. So sometimes I’m idle and I feel bad. So instead of going with somebody to do some pair programming, I say, I’m gonna start this thing. What happens next is that the priority, the focus shifts and we have not adjusted, uh, uh, the, the capacity to the demand and that thing, uh, stay, stays for, for weeks or months, uh, and, and contributes to be an outlier eventually. Um, so I think all, all, all these things can, this, this type of exercise that is very close to engineering can help teams understand how these same principles can be applied to teams. In the end, what we want to make sure is that engineers have time to think, product managers have time to think, designers have time to think, teams have time to think; quality time. So being idle, so thinking is not being idle, and this is something very important that we have to, um, that we have to, uh, communicate. And, um, some of the changes we did in our team and, and, and I, I started talking about pitfalls because it’s very easy when you start a team and metrics to try to optimize the metrics by making people busy and, and you normally get the opposite effect. Uh, some of the changes we did was limiting work in progress. Um, you don’t want to have five people in a team, everybody working on a project alone. That’s, uh, that might, might with a, uh, big quotes here give you a boost in, in, in, in speed, or however you want to call it in the short term, but in the long term it’s, it’s, it’s bad. So all this knowledge is gonna be lost when people have to, to, to leave or have to whatever, uh, projects are gonna get sold. So the second thing we did is we favored, uh, pair programming, even more programming. Yeah. Um, so all, all these, all these changes contributed in the end to, um, by, yeah, by do, doing less and achieving more. And sometimes the best thing to go fast is not to, not to start new work.
Kovid Batra: Makes sense. Great.
Kshitij Mohan: ‘Going faster is not necessarily the fastest way to go’ is basically what you wanna say.
Mario Viktorov Mechoulam: Say again?
Kshitij Mohan: Going fast is not always the fastest way to go.
Mario Viktorov Mechoulam: Exactly, exactly.
Kovid Batra: Great, guys. I think, uh, we break for a minute. Uh, we have now the QA session for 15 minutes, so there would be some questions that our audience would like to ask. Uh, so I request the audience to put in all the questions that they have in the next two minutes, uh, so that we can take it up till that time. Uh, you have some music to hear.
Mario Viktorov Mechoulam: Okay.
Kovid Batra: All right. Uh, we have a few questions here now. Uh, I think we can, we are good to go. Uh, I’ll just highlight the first one. This comes from Daniel. How do you handle the continuous nature of the cycle? For example, there might not be one moment of prioritization or there might be a break because of other business priorities taking over between prioritization and engineering started. Interesting question. I think we touched a little bit on this, but Mario, uh, please take this up.
Mario Viktorov Mechoulam: Yes, very good question. Uh, to me, this, this is, uh, very much connected with, uh, how many companies do roadmap and delivery plans. I think we’re often planning way too much ahead and in doing so, we are, um, investing into, into effort and time that is then never capitalized on. So one, one way to, to break this, uh, is to, to do less things. Um, and this applies, uh, both. So I’m, I’m, I’m not against roadmap. I think roadmaps are great. The problem is, is when you start treating your roadmap as a delivery plan or as a strategy, and as you have a, uh, deadlines that are fixed. Um, so one way to do that is do, do less things. What this means is that, uh, if we try to put it to perspective where many teams work together, uh, there is always a chance that something new comes up that, uh, changes the plan that you have so far. So what is the least amount of work that you can start so that you have the minimal risk of being blocked, interrupted and having to switch to something else? And that’s normally one project, maybe two projects. If you, if you have a big team, uh, the same thing can be applied within the team. Uh, I think this connects a bit to one of the points I was making about adjusting, um, capacity to demand. Uh, it’s, uh, there is, if you, if you use Kanban for example, but I’m sure that other, uh, methodologies have different ways. There’s this replenishment meaning, so putting something on the board means that there is a commitment to see it through all the way to the end. So if we are not sure about the priority that something is gonna, uh, is having today, or if we cannot be confident enough that this thing is gonna maintain the, the same priority throughout its cycle, the safety thing might be not to start it to work on something else with somebody else, uh, within your team.
Kovid Batra: And I think I would just like to add here on this question, Daniel, uh, probably if you want to, like, there are unavoidable situations also, we know that, and you need to jump onto other tasks. Uh, then at least, uh, if you’re looking at it from the metrics perspective, that it is gonna, um, like change the complete picture of your cycle time because one of the tasks is lying there and taking too much time. Uh, we can actually go ahead and build a system that either exclude those PRs that are going high in time in the cycle time, or in fact there can be a system to probably reduce the number of hours that just went in vain where there was no activity. So if, if that was something that you were also trying to understand or ask here, uh, that was my take on this question. Yeah.
Mario Viktorov Mechoulam: One, one comment on my end from this, I think, uh, you, you can definitely do that. Uh, there is one, one side effect of doing this though, and which is you won’t be able to communicate, um, to, to the business or to the, uh, people making this decision what is the impact it has on the team. So sometimes it’s, I think it’s better that if we look at metrics, we feel bad, we feel uncomfortable because that’s the trigger to try to do, uh, to try to do something about it. Uh, but while you were talking, Kovid, I, um, I think you, you mentioned something, uh, something important that, that resonated with me and, and gave me another idea, which is, uh, if you work small, um, I think we, yeah, if we manage to work in around, uh, one to three days, uh, task, uh, we can afford to delay something no matter how urgent it is or almost no matter how urgent it is, for a few, uh, for a couple days, uh, to make sure that this is..
Kovid Batra: So, that’s also a good solution here actually, breaking your work into batches, small batches. I think that would be great. Uh, creating small batch of work would really help there. Yeah. Perfect. Uh, moving on to the next question that’s from Nisha. Uh, how do you recommend balancing speed and stability when optimizing DORA metrics? Yeah, I think, uh, uh, yeah, go ahead. Please go ahead.
Mario Viktorov Mechoulam: Okay. Uh, so my, my, my first reflection on this is that speed and stability are the same thing. At least they should be. You want stability to have speed. So, uh, I am of the opinion that unless you’re a startup and you’re at the point where it, it really depends on a few weeks, whether you’re running out of budget or not, or you sign up these, these critical clients, uh, normally stability is gonna have the long term pros, uh, better effects on, on speed. So for me, if you, if you, um, if we are realizing that we are not fast enough to deploy a fix, that to fix and deploy a resolution, uh, that, that’s the first point that we should start addressing.
Kshitij Mohan: Sure. I think, uh, I’ll also just add to it, uh, one, one critical point to what we realized is also depends on the nature of your work or the market that you are in. So what we have realized is, so there are some kinds of users to whom you cannot ship unstable things, you cannot ship unstable features. Uh, whereas when you are going on for maybe some kind of a novelty or just a tryout or a beta thing, then definitely, uh, speed matters because it’s okay if, if it breaks out as well. Uh, but I think that’s also a call that you would have to take on what exactly the situation is and where you have the leeway of prioritizing what, but yeah, that, that’s, that’s the call that, that also comes into play.
Mario Viktorov Mechoulam: Yeah. Very good point.
Kovid Batra: Okay. Uh, I think, uh, we have a question from Narendra. There are different, uh, formula available to calculate metrics. Example, lead time for change is calculated by the time taken for the master branch on production. Uh, on some other sites it says it should be time when the task was taken in into development till the deployment. What is the idle way for this? Okay. Uh, Mario, you wanna take it or Kshitij I think you, you are equally excited to take this up?
Kshitij Mohan: No, I think, uh, fair question, Narendra. So I think it’s a funny take as well, uh, because everyone has their own ways of suggesting what it is, but it, it matters on what you want to actually measure. So basically, if you want to measure your lead time to change and let’s say if you’re in a situation where you would want to understand the time it takes from the inception of when we thought about this work to getting started to actually finally moving into production, you go by, uh, what, what you find a firm, you what, what you found a formula of calculating it from the inception time. But let’s say if you are in a regular, uh, scrum-based approach or you are looking at more on a continuous, uh, CI/CD type of a ecosystem, where you are at, then you might want to look at just the point where you started the work and the time it finally got merged into any of your branches that you would consider. So the way we recommend is, is actually, it should be configurable in a way on what it suits for different teams and different businesses. But yeah, uh, there is no one defined, uh, I would say formula for, for seeing this. You need to look at it at a broader perspective on what exactly translates into your, uh, basically engineering ecosystem. So that’s, that’s what my take would be. But yeah, Mario, if you have anything to add, please, please feel free.
Mario Viktorov Mechoulam: Perfect answer. You, you read my mind.
Kovid Batra: Great. I think we can move on to the next one then. Uh, it’s from Priya. Are there any new or emerging metrics that you believe will become industry standards soon?
Mario Viktorov Mechoulam: Um, good, good question. I don’t know. Uh, I think we are far away from industry standards for, for many things. And, and when there have been some industry standards, often they have been wrong or let’s say miss, things are being mismeasured. Um, what do you think, Mohan? Is there anything?
Kshitij Mohan: Yeah, I think that’s.. Yeah, I think, uh, firstly it’s a very good question to think about that how, uh, this current ecosystem is changing. And I think with the advent of new frameworks coming in, like SPACE, DevEx, DORA, and, and a lot more now coming into, so it’s, it’s, I think it’s a pretty, uh, interesting inflection point is what we see this entirely, uh, I would say the whole metric defining pieces. So, as you mentioned, we are not sure what becomes the norm, uh, but what I feel is they’re not going to be any, uh, one specific way to measure things because what we are seeing is every team, every, uh, organization, every stage that your company’s at, there are so many different nuances that everyone wants to track and understand. Uh, so I believe, I don’t think so there is gonna be, uh, one specific metric, but there are definitely going to be a lot more frameworks coming in, uh, over the next few years that’s gonna help us maybe identify and define the metrics in a better way.
Kovid Batra: Perfect. Uh, moving on. Uh, I think this is the last one we can take. Uh, this is from Vishal. Uh, how do DORA Metrics correlate with business outcomes like revenue growth or customer satisfaction in your experience? Uh, very relevant question I think. Uh, Mario, would you like to take this up?
Kshitij Mohan: Yup.
Mario Viktorov Mechoulam: Yes, sure. Um, so there is, there is not a direct link, right? So your, your metrics, uh, are information that you can use to improve how you ship things. How you ship things, uh, is able, allows you to create business impact, which might not always be direct to the customer. So maybe your business impact is our pipelines are now better, more stable, they’re faster. And by having this type of business impact, we can easily unlock business value. Um, to make sure that the business value is the outcome that we want, we need to also measure. So this is what, when I, when I was talking about, uh, effectiveness metrics, these are normally the business or the product metrics. Uh, so we have to, uh, of course, have everything before that, uh, including a very fast pipeline so we can correlate the, the changes that we ship with the, with the changes and the outcomes that we see.
Kovid Batra: Totally. I think, yeah, uh, so there is no direct impact, but there is definitely, uh, a goal related impact that we can see and there are a few examples that I would like to share here. Uh, like change failure rate, uh, for, for that matter, could be something that you might find very close to, uh, not revenue, but at least customer satisfaction because if the failure rate is higher, then the customer satisfaction will definitely drop down. So you can easily find a correlation there with at least that metric. But metrics like, uh, cycle time, for example, you might not necessarily find a direct impact, but if it’s better, it is really gonna help you ship more features faster, and that ultimately could help you maybe at what stage you are finding a PMF or maybe having more revenue through the next feature that you just released. So, yeah, that, that’s my answer to Vishal’s question.
Kshitij Mohan: And definitely, and I think just to add one last thing to it. So fundamentally we, uh, talk a lot about DORA, uh, but I think somehow this is something that I find very less talked about is that fundamentally DORA is trying to capture, uh, simplistically the velocity, quality, throughput of your systems. And these could be translated into multiple ways and into multiple forms. But if used in the right effective way, there would be some ways that you can start impacting and understanding the overall outcomes. But yeah, they have to be now correlated with what makes, what metrics make more sense to you. So if you already have some key metrics, if you start fitting them around the DORA ecosystem, then that really works well is what we have seen. But yeah, uh, it kind of, uh, depends on your use case as well.
Kovid Batra: Great, Kshitij. Thank you so much. Uh, I think guys, uh, we are, uh, that’s our time, actually. Uh, but before we just go, uh, both of you, uh, it was really great talking to you, uh, learning from you both. Uh, any parting thoughts that you would like to share? Uh, I think we’ll go with Mario first. Uh, Mario, any, any parting thoughts for the audience?
Mario Viktorov Mechoulam: Yes. Um, I hope I’m not repeating myself, but, uh, when we talk about the pitfall of 100% utilization, um, we have to take that into, into the whole business, uh, domain. So oftentimes we are hearing people need to innovate. People need to automate. People need to improve. People need to this. People need to that. It, it’s a zero sum game, right? So if we want people to do that, that type of things, there needs to be a change in culture that accompanies the, the words we talk. And when we do that, we have to be mindful of this, of, of Kafka, right, of the stability of the system if we want people to invest there. And as leaders, as managers, it’s our responsibility to, to enable that.
Kshitij Mohan: Totally. Perfect. I think just to add on to what Mario said. So, exactly. So it should not be a zero sum game. It should be a positive sum game. I think this is all how we should be defining everything for, for the entire ecosystem out there.
Kovid Batra: Alright, thank you. Thank you guys. Uh, it was a pleasure having you both here and there are many more such sessions to come. And Mario, we’ll keep bugging you, uh, to share your insights on those. Till then, uh, this is our time guys. Thank you so much.
Mario Viktorov Mechoulam: Thank you.
Kshitij Mohan: Thank you. Thanks, Mario. Thank you so much.
How do you build a culture of engineering metrics that drives real impact? Engineering teams often struggle with inefficiencies — high work-in-progress, unpredictable cycle times, and slow shipping. But what if the right metrics could change that?
In this episode of the groCTO by Typo Podcast, host Kovid Batra speaks with Mario Viktorov Mechoulam, Senior Engineering Manager at Contentsquare, about how to establish a data-driven engineering culture using effective metrics. From overcoming cultural resistance to getting executive buy-in, Mario shares his insights on making metrics work for your team.
✅ Why Metrics Matter: How the lack of metrics creates inefficiencies & frustrations in tech teams.
✅ Building a Metrics-Driven Culture: The five key steps — observability, accountability, understanding, discussions, and agreements.
✅ Overcoming Resistance: How to tackle biases, cultural pushback, and skepticism around metrics.
✅ Practical Tips for Engineering Managers: Early success indicators like reduced work-in-progress & improved predictability.
✅ Getting Executive Buy-In: How to align leadership on the value of engineering metrics.
✅ A Musician’s Path to Engineering Metrics: Mario’s unique journey from music to Lean & Toyota Production System-inspired engineering.
Kovid Batra: Hi, everyone. Welcome to the all new episode of groCTO by Typo. This is Kovid, your host. Today with us, we have a very special guest whom I found after stalking a lot of people on LinkedIn, but found him in my nearest circle. Uh, welcome, welcome to the show, Mario. Uh, Mario is a Senior Engineering Manager at Contentsquare and, uh, he is an engineering metrics enthusiast, and that’s where we connected. We talked a lot about it and I was sure that he’s the guy we should have on the podcast to talk about it. And that’s why we thought today’s topic should be something that is very close to Mario, which is setting metrics culture in the engineering teams. So once again, welcome, welcome to the show, Mario. It’s great to have you here.
Mario Viktorov Mechoulam: Thank you, Kovid. Pleasure is mine. I’m very happy to join this series.
Kovid Batra: Great. So Mario, I think before we get started, one quick question, so that we know you a little bit more. Uh, this is kind of a ritual we always have, so don’t get surprised by it. Uh, tell us something about yourself from your childhood or from your teenage that defines who you are today.
Mario Viktorov Mechoulam: Right. I think my, my, both of my parents are musicians and I played violin for a few years, um, also in the junior orchestra. I think this contact with music and with the orchestra in particular, uh, was very important to define who I am today because of teamwork and synchronicity. So, orchestras need to work together and need to have very, very good collaboration. So, this part stuck somewhere on the back of my brain. And teamwork and collaboration is something that defines me today and I value a lot in others as well.
Kovid Batra: That’s really interesting. That is one unique thing that I got to learn today. And I’m sure orchestra must have been fun.
Mario Viktorov Mechoulam: Yes.
Kovid Batra: Do you do that, uh, even today?
Mario Viktorov Mechoulam: Uh, no, no, unfortunately I’m, I’m like the black sheep of my family because I, once I discovered computers and switched to that, um, I have not looked back. Uh, some days I regret it a bit, uh, but this new adventure, this journey that I’m going through, um, I don’t think it’s, it’s irreplaceable. So I’m, I’m happy with what I’m doing.
Kovid Batra: Great! Thank you for sharing this. Uh, moving on, uh, to our main section, which is setting a culture of metrics in engineering teams. I think a very known topic, a very difficult to do thing, but I think we’ll address the elephant in the room today because we have an expert here with us today. So Mario, I think I’ll, I’ll start with this. Uh, sorry to say this, but, uh, this looks like a boring topic to a lot of engineering teams, right? People are not immediately aligned towards having metrics and measurement and people looking at what they’re doing. And of course, there are biases around it. It’s a good practice. It’s an ideal practice to have in high performing engineering teams. But what made you, uh, go behind this, uh, what excited you to go behind this?
Mario Viktorov Mechoulam: A very good question. And I agree that, uh, it’s not an easy topic. I think that, uh, what’s behind the metrics is around us, whether we like it or not. Efficiency, effectiveness, optimization, productivity. It’s, it’s in everything we do in the world. So, for example, even if you, if you go to the airport and you stay in a queue for your baggage check in, um, I’m sure there’s some metrics there, whether they track it or not, I don’t know. And, um, and I discovered in my, my university years, I had, uh, first contact with, uh, Toyota production system with Lean, how we call it in the West, and I discovered how there were, there were things that looked like, like magic that you could simply by observing and applying use to transform the landscape of organizations and the landscape systems. And I was very lucky to be in touch with this, uh, with this one professor who is, uh, uh, the Director of the Lean Institute in Spain. Um, and I was surprised to see how no matter how big the corporation, how powerful the people, how much money they have, there were inefficiencies everywhere. And in my eyes, it looks like a magic wand. Uh, you just, uh, weave it around and then you magically solve stuff that could not be solved, uh, no matter how much money you put on them. And this was, yeah, this stuck with me for quite some time, but I never realized until a few years into the industry that, that was not just for manufacturing, but, uh, lean and metrics, they’re around us and it’s our responsibility to seize it and to make them, to put them to good use.
Kovid Batra: Interesting. Interesting. So I think from here, I would love to know some of the things that you have encountered in your journey, um, as an engineering leader. Uh, when you start implementing or bringing this thought at first point in the teams, what’s their reaction? How do you deal with it? I know it’s an obvious question to ask because I have been dealing with a lot of teams, uh, while working at Typo, but I want to hear it from you firsthand. What’s the experience like? How do you bring it in? How do you motivate those people to actually come on board? So maybe if you have an example, if you have a story to tell us from there, please go ahead.
Mario Viktorov Mechoulam: Of course, of course. It’s not easy and I’ve made a lot of mistakes and one thing that I learned is that there is no fast track. It doesn’t matter if you know, if you know how to do it. If you’ve done it a hundred times, there’s no fast track. Most of the times it’s a slow grind and requires walking the path with people. I like to follow the, these steps. We start with observability, then accountability, then understanding, then discussions and finally agreements. Um, but of course, we cannot, we cannot, uh, uh, drop everything at, at, at, at once at the team because as you said, there are people who are generally wary of, of this, uh, because of, um, bad, bad practices, because of, um, unmet expectations, frustrations in the past. So indeed, um, I have, I have had to be very, very careful about it. So to me, the first thing is starting with observability, you need to be transparent with your intentions. And I think one, one key sentence that has helped me there is that trying to understand what are the things that people care about. Do you care about your customers? Do you care about how much focus time, how much quality focus time do you have? Do you care about the quality of what you ship? Do you care about the impact of what you ship? So if the answer to these questions is yes, and for the majority of engineers, and not only engineers, it’s, it’s yes, uh, then if you care about something, it might be smart to measure it. So that’s a, that’s a good first start. Um, then by asking questions about what are the pains or generating curiosity, like for example, where do you think we spend the most time when we are working to ship something? You can, uh, you can get to a point where the team agrees to have some observability, some metrics in place. So that’s the first step.
Uh, the second step is to generate accountability. And that is arguably harder. Why so? Because in my career, I’ve seen sometimes people, um, who think that these are management metrics. Um, and they are, so don’t get me wrong. I think management can put these metrics to good use, um, but this sends a message in that nobody else is responsible for them, and I disagree with this. I think that everybody is responsible. Of course, I’m ultimately responsible. So, what I do here is I try to help teams understand how they are accountable of this. So if it was me, then I get to decide how it really works, how they do the work, what tools they use, what process they use. This is boring. It’s boring for me, but it’s also boring and frustrating for the people. People might see this as micromanagement. I think it’s, uh, it’s much more intellectually interesting if you get to decide how you do the work. And this is how I connect the accountability so that we can get teams to accept that okay, these metrics that we see, they are a result of how we have decided to work together. The things, the practices, the habits that we do. And we can, we can influence them.
Kovid Batra: Totally. But the thing is, uh, when you say that everyone should be onboarded with this thought that it is not just for the management, for the engineering, what exactly, uh, are those action items that you plan that get this into the team as a culture? Because I, I feel, uh, I’ll touch this topic again when we move ahead, but when we talk about culture, it comes with a lot of aspects that you can, you can not just define, uh, in two days or three days or five days of time. There is a mindset that already exists and everything that you add on top of it comes only or fits only if it aligns with that because changing culture is a hard thing, right? So when you say that people usually feel that these are management metrics, somehow I feel that this is part of the culture. But when you bring it, when you bring it in a way that everyone is accountable, bringing that change into the mindset is, is, is a little hard, I feel. So what exactly do you do there is what I want to understand from you.
Mario Viktorov Mechoulam: Sure. Um, so just, just to be, to be clear, at the point where you introduce this observability and accountability, it’s not, it’s not part of the culture yet. I think this is the, like, putting the foot on the door, uh, to get people to start, um, to start looking at these, using these and eventually they become a culture, but way, way later down the line.
Kovid Batra: Got it, got it. Yeah.
Mario Viktorov Mechoulam: Another thing is that culture takes, takes a lot of time. It’s, uh, um, how can we say? Um, organic adoption is very slow. And after organic adoption, you eventually get a shifting culture. Um, so I was talking to somebody a few weeks back, and they were telling me a senior leader for one of another company, and they were telling me that it took a good 3–4 years to roll out metrics in a company. And even then, they did not have all the levels of adoption, all the cultural changes everywhere in all the layers that they wanted to. Um, so, so this, there’s no fast track. This, this takes time. And when you say that, uh, people are wary about metrics or people think that manage, this is management metrics when they, when, when you say this is part of culture, it’s true. And it comes maybe from a place where people have been kept out of it, or where they have seen that metrics have been misused to do precisely micromanagement, right?
Kovid Batra: Right.
Mario Viktorov Mechoulam: So, yeah, people feel like, oh, with this, my work is going to be scrutinized. Perhaps I’m going to have to cut corners. I’m going to be forced to cut corners. I will have less satisfaction in the work we do. So, so we need to break that, um, to change the culture. We need to break the existing culture and that, that takes time. Um, so for me, this is just the first step. Uh, just the first step to, um, to make people feel responsible, because at the end of the day, um, every, every team costs some, some, some budget, right, to the company. So for an average sized team, we might be talking $1 million, depending on where you’re located, of course. But $1 million per year. So, of course, this, each of these teams, they need to make $1 million in, uh, in impact to at least break even, but we need more. Um, how do we do that? So two things. First, you need, you need to track the impact of the work you do. So that already tells you that if we care about this, there is a metric that we have to incorporate. We have to track the impact, the effect that the work we ship has in the product. But then the second, second thing is to be able to correlate this, um, to correlate what we ship with the impact that we see. And, and there is a very, very, uh, narrow window to do that. You cannot start working on something and then ship it three years later and say, Oh, I had this impact. No, in three years, landscape changed a lot, right? So we need to be quicker in shipping and we need to be tracking what we ship. Therefore, um, measuring lead time, for example, or cycle time becomes one of the highest expressions of being agile, for example.
Kovid Batra: Got it.
Mario Viktorov Mechoulam: So it’s, it’s through these, uh, constant repetition and helping people see how the way they do work, how, whether they track or not, and can improve or not, um, has repercussions in the customer. Um, it’s, it’s the way to start, uh, introducing this, this, uh, this metric concept and eventually helping shift the culture.
Kovid Batra: So is, let’s say cycle time for, for that matter, uh, is, is a metric that is generally applicable in every situation and we can start introducing it at, at the first step and then maybe explore more and, uh, go for some specifics or cycle time is specific to a situation in itself?
Mario Viktorov Mechoulam: I think cycle time is one of these beautiful metrics that you can apply everywhere. Uh, normally you see it applied on the teams. To do, doing, done. But, uh, what I like is that you can apply it, um, everywhere. So you can apply it, um, across teams, you can apply, apply it at line level, you can even apply it at company level. Um, which is not done often. And I think this is, this is a problem. But applying it outside of teams, it’s definitely part of the cultural change. Um, I’ve seen that the focus is often on teams. There’s a lot of focus in optimizing teams, but when you look at the whole picture, um, there are many other places that present opportunities for optimization, and one way to do that is to start, to start measuring.
Kovid Batra: Mario, did you get a chance where you could see, uh, or compare basically, uh, teams or organizations where people are using engineering metrics, and let’s say, a team which doesn’t use engineering metrics? How does the value delivery in these systems, uh, vary, and to what extent, basically?
Mario Viktorov Mechoulam: Let me preface that. Um, metrics are just a cornerstone, but they don’t guarantee that you’d do better or worse than the teams that don’t apply them. However, it’s, it’s very hard, uh, sometimes to know whether you’re doing good or bad if you don’t have something measurable, um, to, to do that. What I’ve seen is much more frustration generally in teams that do not have metrics. But because not having them, uh, forces them into some bad habits. One of the typical things that I, that I see when I join a team or do a Gemba Walk, uh, on some of the teams that are not using engineering metrics, is high work in progress. We’re talking 30+ things are ongoing for a team of five engineers. This means that on average, everybody’s doing 5–6 things at the same time. A lot of context switching, a lot of multitasking, a lot of frustration and leading to things taking months to ship instead of days. Of course, as I said, we can have teams that are doing great without this, but, um, if you’re already doing this, I think just adding the metric to validate it is a very small price to pay. And even if you’re doing great, this can start to change in any moment because of changes in the team composition, changes in the domain, changes in the company, changes in the process that is top-down. So it’s, uh, normally it’s, it’s, it’s very safe to have the metrics to be able to identify this type of drift, this type of degradation as soon as they happen. What I’ve seen also with teams that do have metric adoption is first this eventual cultural change, but then in general, uh, one thing that they do is that they keep, um, they keep the pieces of work small, they limit the work in progress and they are very, very much on top of the results on a regular basis and discussing these results. Um, so this is where we can continue with the, uh, cultural change.
Uh, so after we have, uh, accountability, uh, the next thing, step is understanding. So helping people through documentation, but also through coaching, understand how the choices that we make, the decisions, the events, produce the results that we see for which we’re responsible. And after that, fostering discussion for which you need to have trust, because here we don’t want blaming. We don’t want comparing teams. We want to understand what happened, what led to this. And then, with these discussions, see what can we do to prevent these things. Um, which leads to agreement. So doing this circle, closing the circle, doing it constantly, creates habits. Habits create continuous improvement, continuous learning. And at a certain point, you have the feeling that the team already understands the concepts and is able to work autonomously on this. And this is the moment where you delegate responsibility, um, of this and of the execution as well. And you have created, you have changed a bit the culture in one team.
Kovid Batra: Makes sense. What else does it take, uh, to actually bring in this culture? What else do you think is, uh, missing in this recipe yet?
Mario Viktorov Mechoulam: Yes. Um, I think working with teams is one thing. It’s a small and controlled environment. But the next thing is that you need executive sponsorship. You need to work at the organization level. And that is, that is a bit harder. Let’s say just a bit harder. Um, why is it hard?
Kovid Batra: I see some personal pain coming in there, right?
Mario Viktorov Mechoulam: Um, well, no, it depends. I think it can be harder or it can be easier. So, for example, uh, my experience with startups is that in general, getting executive sponsorship there, the buy-in, is way easier. Um, at the same time, the, because it’s flatter, so you’re in contact day to day with the people who, who need to give you this buy-in. At the same time, very interestingly, engineers in these organizations often are, often need these metrics much less at that point. Why? Because when we talk about startups, we’re talking about much less meetings, much less process. A lot of times, a lot of, um, people usually wear multiple hats, boundaries between roles are not clear. So there’s a lot of collaboration. People usually sit in the very same room. Um, so, so these are engineers that don’t need it, but it’s also a good moment to plant the seed because when these companies grow, uh, you’ll be thankful for that later. Uh, where it’s harder to get it, it’s in bigger corporations. But it’s in these places where I think that it’s most needed because the amount of process, the amount of bureaucracy, the amount of meetings, is very, very draining to the teams in those places. And usually you see all these just piles up. It seldom gets removed. Um, that, maybe it’s a topic for a different discussion. But I think people are very afraid of removing something and then be responsible of the result that removal brings. But yeah, I have, I have had, um, we can say fairly, a fair success of also getting the executive sponsorship, uh, in, in organizations to, to support this and I have learned a few things also along the way.
Kovid Batra: Would you like to share some of the examples? Not specifically from, let’s say, uh, getting sponsorship from the executives, I would be interested because you say it’s a little hard in places. So what things do you think, uh, can work out when you are in that room where you need to get a buy-in on this? What exactly drives that?
Mario Viktorov Mechoulam: Yes. The first point is the same, both for grassroots movements with teams and executive sponsorship, and that is to be transparent. Transparent with what, what do you want to do? What’s your intent and why do you think this is important? Uh, now here, and I’m embarrassed to say this, um, we, we want to change the culture, right? So we should focus on talking about habits, um, right? About culture, about people, et cetera. Not that much about, um, magic to say that, but I, but I’m guilty of using that because, um, people, people like how this sounds, people like to see, to, to, to hear, oh, we’ll introduce metrics and they will be faster and we’ll be more efficient. Um, so it’s not a direct relationship. As I said, it’s a stepping stone that can help you get there. Um, but, but it’s not, it’s not a one month journey or a one year journey. It can take slightly longer, but sometimes to get, to get the attention, you have to have a pitch which focuses more on efficiency, which focuses more on predictability and these type of things. So that’s definitely one, one learning. Um, second learning is that it’s very important, no matter who you are, but it’s even more important when you are, uh, not at the top of the, uh, of the management, uh, uh, pyramid to get, um, by, uh, so to get coaching from your, your direct manager. So if you have somebody that, uh, makes your goals, your objectives, their own, uh, it’s great because they have more experience, uh, they can help you navigate these and present the cases, uh, in a much better and structured way for the, for the intent that you have. And I was very lucky there as well to count on people that were supportive, uh, that were coaching me along the way. Um, yes.
So, first step is the same. First step is to be transparent and, uh, with your intent and share something that you have done already. Uh, here we are often in a situation where you have to put your money where your mouth is, and sometimes you have to invest from your own pocket if you want, for example, um, to use a specific tool. So to me, tools don’t really matter. So what’s important is start with some, something and then build up on top of it, change the culture, and then you’ll find the perfect tool that serves your purpose. Um, exactly. So sometimes you have to, you have to initiate this if you want to have some, some, some metrics. Of course, you can always do this manually. I’ve done it in the past, but I definitely don’t recommend it because it’s a lot of work. In an era where most of these tools are commodities, so we’re lucky enough to be able to gather this metric, this information. Yeah, so usually after this PoC, this experiment for three to six months with the team, you should have some results that you can present, um, to, um, to get executive sponsorship. Something that’s important here that I learned is that you need to present the results very, very precisely. Uh, so what was the problem? What are the actions we did? What’s the result? And that’s not always easy because when you, when you work with metrics for a while, you quickly start to see that there are a lot of synergies. There’s overlapping. There are things that impact other things, right? So sometimes you see a change in the trend, you see an improvement somewhere, uh, you see the cultural impact also happening, but you’re not able to define exactly what’s one thing that we need or two things that we, that we need to change that. Um, so, so that part, I think is very important, but it’s not always easy. So it has to be prepared clearly. Um, the second part is that unfortunately, I discovered that not many people are familiar with the topics. So when introducing it to get the exact sponsorship, you need to, you need to be able to explain them in a very simple, uh, and an easy way and also be mindful of the time because most of the people are very busy. Um, so you don’t want to go in a full, uh, full blown explanation of several hours.
Kovid Batra: I think those people should watch these kinds of podcasts.
Mario Viktorov Mechoulam: Yeah. Um, but, but, yeah, so it’s, it’s, it’s the experiment, it’s the results, it’s the actions, but also it’s a bit of background of why is this important and, um, yeah, and, and how did it influence what we did.
Kovid Batra: Yeah, I mean, there’s always, uh, different, uh, levels where people are in this journey. Let’s, let’s call this a journey where you are super aware, you know what needs to be done. And then there is a place where you’re not aware of the problem itself. So when you go through this funnel, there are people whom you need to onboard in your team, who need to first understand what we are talking about what does it mean, how it’s going to impact, and what exactly it is, in very simple layman language. So I totally understand that point and realize that how easy as well as difficult it is to get these things in place, bring that culture of metrics, engineering metrics in the engineering teams.
Well, I think this was something really, really interesting. Uh, one last piece that I want to touch upon is when you put in all these efforts into onboarding the teams, fostering that culture, getting buy-in from the executives, doing your PoCs and then presenting it, getting in sync with the team, there must be some specific indicators, right, that you start seeing in the teams. I know you have just covered it, but I want to again highlight that point that what exactly someone who is, let’s say an engineering manager and trying to implement it in the team should be looking for early on, or let’s say maybe one month, two months down the line when they started doing that PoC in their teams.
Mario Viktorov Mechoulam: I think, um, how comfortable the people in the team get in discussing and explaining the concepts during analysis of the metrics, this quality analysis is key. Um, and this is probably where most of the effort goes in the first months. We need to make sure that people do understand the metrics, what they represent, how the work we do has an impact on those. And, um, when we reached that point, um, one, one cue for me was the people in my teams, uh, telling me, I want to run this. This meant to me that we had closed the circle and we were close to having a habit and people were, uh, were ready to have this responsibility delegated to them to execute this. So it put people in a place where, um, they had to drive a conversation and they had to think about okay, what am I seeing? What happened? But what could it mean? But then what actions do we want to take? But this is something that we saw in the past already, and we tried to address, and then maybe we made it worse. And then you should also see, um, a change in the trend of metrics. For example, work in progress, getting from 30+ down to something close to the team size. Uh, it could be even better because even then it means that people are working independently and maybe you want them to collaborate. Um, some of the metrics change drastically. Uh, we can, we can talk about it another time, but the standard deviation of the cycle time, you can see how it squeezes, which means that, uh, it, it doesn’t, uh, feel random anymore. When, when I’m going to ship something, but now right now we can make a very, um, a very accurate guess of when, when it’s going to happen. So these types of things to me, mark, uh, good, good changes and that you’re on the right path.
Kovid Batra: Uh, honestly, Mario, very insightful, very practical tips that I have heard today about the implementation piece, and I’m sure this doesn’t end here. Uh, we are going to have more such discussions on this topic, and I want to deep dive into what exact metrics, how to use them, what suits which situation, talking about things like standard deviation from your cycle time would start changing, and that is in itself an interesting thing to talk about. So probably we’ll cover that in the next podcast that we have with you. For today, uh, this is our time. Any parting advice that you would like to share with the audience? Let’s say, there is an Engineering Manager. Let’s say, Mario five years back, who is thinking to go in this direction, what piece of advice would you give that person to get on this journey and what’s the incentive for that person?
Mario Viktorov Mechoulam: Yes. Okay. Clear. In, in general, you, you’ll, you’ll hear that people and teams are too busy to improve. We all know that. So I think as a manager who wants to start introducing these, uh, these concepts and these metrics, your, one of your responsibilities is to make room, to make space for the team, so that they can sit down and have a quality, quality time for this type of conversation. Without it, it’s not, uh, it’s not going to happen.
Kovid Batra: Okay, perfect. Great, Mario. It was great having you here. And I’m sure, uh, we are recording a few more sessions on this topic because this is close to us as well. But for today, this is our time. Thank you so much. See you once again.
Mario Viktorov Mechoulam: Thank you, Kovid. Pleasure is mine. Bye-bye!
Kovid Batra: Bye.
In the third session of 'Unlocking Engineering Productivity' webinar by Typo, host Kovid Batra converses with engineering leaders Clint Calleja and Rens Methratta about strategies for enhancing team productivity.
Clint, Senior Director of Engineering at Contentsquare, and Rens, CTO at Prendio, share their perspectives on the importance of psychological safety, clear communication, and the integration of AI tools to boost productivity. The panel emphasizes balancing short-term deliverables with long-term technical debt, and the vital role of culture and clear goals in aligning teams. Through discussions on personal experiences, challenges, and learnings, the session provides actionable insights for engineering managers to improve developer experience and foster a collaborative working environment.
Kovid Batra: All right. Welcome to the third session of Unlocking Engineering Productivity. This is Kovid, your host and with me today we have two amazing, passionate engineering leaders, Clint and Rens. So I’ll introduce them one by one. Let’s go ahead. Uh, Clint, uh, he’s the senior Director of engineering at Contentsquare, ex-Hotjar, uh, a long time friend and a mentor. Uh, welcome, welcome to the show, Clint. It is great to have you here.
Clint Calleja: Thank you. Thank you, Kovid. It’s, uh, it’s uh, it’s very exciting to be here. Thank you for the invite.
Kovid Batra: Alright. Uh, so Clint, uh, I think we were talking about your hobbies last time and I was really, uh, fascinated by the fact. So guys, uh, Clint is actually training in martial arts. Uh, he’s very, very, uh, well trained professional martial arts player and he’s particularly, uh, more interested in karate. Is it right, Clint?
Clint Calleja: Yes. Yes indeed. It’s, uh, I wouldn’t say professionally, you know, we’ve been at it for two years, me and the kids. But yes, it’s, uh, it’s grown on me. I enjoy it.
Kovid Batra: Perfect. What else do you like? Uh, would you like to share something about your hobbies and passions?
Clint Calleja: Yeah, I’m, I’m pretty much into, um, on the, you know, more movement side. Uh, I’m, I’m, I’m into sports in general, like fit training, and I enjoy a game of squash here and there. And then on the commerce side, I need my, you know, daily dose of reading. It, it varies. Sometimes it’s, uh, around leadership, sometimes psychology. Uh, lately it’s a bit more also into stoicism and the art of controlling, you know, thinking about what we can control. Uh, yeah, that’s, that’s me basically.
Kovid Batra: That’s great. Really interesting. Um, the, another guest that we have today, uh, we have Rens with us. Uh, Rens is CTO of Prendio. Uh, he is also a typo product user and a product champion. He has been guiding us throughout, uh, on building the product so far. Welcome to the show, Rens.
Rens Methratta: Hi, Kovid. Uh, you know, it’s good to be here. Uh, Clint, it’s really good to meet you. Uh, very excited to participate and, uh, uh, it’s always really good to, uh, talk shop. Uh, enjoy it.
Kovid Batra: Thank you so much. Thank you so much. Uh, all right, uh, Rens, would you like to tell us something about your hobbies? How do you unwind your day? What do you do outside of work?
Rens Methratta: Yeah, no, um, it’s funny, I don’t have many, I don’t think I have many hobbies anymore. I mean, I have two young kids now, um, and they are, uh, they take up, their hobbies are my hobbies. So, uh, um, so gymnastics, soccer, um, we have, uh, other, you know, a lot of different sports things and piano. So I, I’ve, I’ve learning, I’m learning piano with my daughter. I guess that’s a hobby. Um, that’s, uh, not far asleep, but I’m, I’m enjoying it. But I think a lot of their things that they do become stuff that, um, I get involved in and I really try to, um, I try to enjoy it with them as well. It makes, it makes it more fun.
Kovid Batra: No, I can totally understand that, because having two kids and, uh, being in a CTO position, uh, I think all your time would be consumed outside of work by the kids. Uh, that’s, that’s totally fine. And if your hobbies are aligned with what your kids are doing, that’s, that’s good for them and good for you.
Rens Methratta: Yeah, no, I, I, I think, I think it’s, I, I, I, I love it. I enjoy it. I, it keeps me, you know, I always say, you know, I think there’s a, I remember learning a long time ago, someone said that, you know, how you, uh, the, when you get older, you know, life, life goes by faster. ’cause you kept on doing the same stuff every day and your mind just samples less, right? So, like, they kind of keep me young. I get to do new stuff, um, with, through them. So it’s, it’s been good.
Kovid Batra: Perfect. Perfect. Um, thanks for the, for the introduction. Uh, we got to know you a little more, but that doesn’t stop here. Uh, Clint, you were talking about psychology reading those books. Uh, there is one small ritual, uh, on, on this show, uh, that is again, driven from my, uh, love for psychology, understanding human behavior and everything. So, uh, the ritual is basically that you have to tell something about yourself, uh, from your childhood, from your teenage, uh, that defines you who you are today.
Clint Calleja: Very interesting question. It reminds me of, uh, of a previous manager I used to have used to like, okay, asking this question as well. I think, um, there was a recent one which we just mentioned. Uh, you know, we’re mentioning kids, Rens, you got me to it. The fact that I actually started training martial arts because of the kids, I took them and I ended up doing it myself. Uh, so it was one of those. But I think the biggest one that comes to mind was, um, in 2005, at the age of 22, um, in Malta, you know, we’re a very tightly-knit culture. Um, uh, people, you know, stay living with their parents long, where a small island, everyone is close by. So I wanted to see what’s out there. So I went to live for a year in Germany. Um, and it was, I think this was the most defining moment because for two France. On one side it was the, um, a career opportunity where whilst I was still studying. So for software engineering part-time, um, there was this company that offered to take me on as an intern, but trained me for a whole year in their offices in Germany. So that was a good, uh, step in the right direction career wise, but, uh, outside, you know, of the profession, just on a personal life basis, it was such an eye-opener. It was, uh, this was the year where I realized, um, how many things were, was I taking for granted? You know, like, uh, family and friends. Yeah. Close by when you need them. Um, even simple things like, you know, the sunny weather in Malta, so the sea close by, like, I think this was the year where I became much more aware of all of this and, uh, could be, could reflect a bit deeper.
Kovid Batra: I totally relate to this actually. Uh, for you it happened, uh, I would say a little late because probably you moved out during your, uh, job or, uh, later in the college. For me, it happened in early teenage, I moved out for schooling for host, hostel and there were same realizations that I had and it got me thinking a lot about what I was taking for granted. So I totally relate and, and, and that actually defined me, who am I today. I’m much more grateful towards my parents and, uh, family that I have with me. Yeah.
Clint Calleja: Exactly. Exactly.
Kovid Batra: Yeah. Yeah, yeah. Totally. Rens, it’s your turn now.
Rens Methratta: Yeah. I, I, yeah. I’m, I’m glad, um, I, thinking through this, it was, it was an interesting question. Um, I think, you know, I, I, I’d say me growing up, I grew up on a, with my grandparents, right. And, and we, we had a farm, and I think growing up with them, obviously them being a bit older, I, I think they had a, you know, that there’s, I think you get older, you get a little bit more sense of maturity, kind of, you know, thinking of the world and seeing that at a young age, I think was really good for me because, you know, they were, you know, in farming there’s lots of things that sometimes go wrong. There’s floods, there’s, uh, disease, there’s, yeah, lots of stuff. But you know how they kind of approach things like, you know, they’re, you know, they were, they were never about, you know, let’s, let’s blame anyone, let’s do this, let’s, you know, really focus on, hey, let’s stay calm. Let’s focus on solving the problem. Let’s figure it out. Kind of staying positive. And, and I think that was really helpful for me. I think in setting an example, and really the biggest thing they taught me was like, you know, there are certain things you just can’t control. You know, just focus on things you can control and worry about those. And, you know, and, and, and, and that’s it. Really be positive in a lot of ways. Yeah. And I, I carry that with me a lot. And I think there’s, you know, there’s a lot of stuff you can stress out about, but there’s only so many things you can control and you kinda let go of everything else. So, so totally. That’s kind of, keep that with me.
Kovid Batra: Totally makes sense. I mean, uh, people like you coming from humble backgrounds are more fighter in nature. Uh, they’re more positive in lives, they’re more optimistic towards such situations. And I’ve seen that in a lot of, a lot of folks around me. Uh, people who are privileged do not really, um, get to be that anti-fragile, uh, when situations come, they break down easily. So I think that defines who you are. I totally relate to that. Perfect. Great. Thank you. Thank you for sharing this. Uh, alright guys. Uh, I think now we will move on to the main section, uh, whatever this particular unlocking engineering productivity is about. Uh, uh, today’s theme is around, uh, developer experience and of course the experience is that you have, you both have gathered over your engineering leadership journey. So I’ll start with a very fundamental thing and I think, uh, we’ll, we’ll go with Rens first. Uh, so let, let’s say, Rens, we, we’ll start. Uh, what according to you is engineering productivity? I mean, that’s the very fundamental question that I ask on this episode, but I want to hear out, the audience would want to understand what is the perspective of engineering leaders of such high performing teams when they talk about productivity?
Rens Methratta: Yeah, I think, you know, a lot of ways I, there there’s, are the, obviously the simple things, metrics you like, um, you know, velocity, things like that. Those are always good. Those are good to have. But from my perspective, um, and the way that I, I think really good teams function is, uh, making sure the teams are aligned with business objectives, right? So what we’re trying to accomplish, common goals, um, regardless of, you know, how big an organization is, I think if, um, and it gets harder when you get bigger, obviously, right? It’s like identifying, uh, the layers between your impact and then maybe the business is higher. Maybe it’s easier for smaller teams. Um, but, but regardless, I think that’s, that’s always been what I’ve seen that the outcome, a linking to the outcomes that makes the most sense, and understanding productivity. So like, hey, these are, this is, this is what their goals are. You know, I think OKRs work really well in terms of structuring that as a, as a framework. Right. But realistically I’m saying, Hey, here’s, here’s what we as a team are trying to accomplish. Uh, you know, here’s how we’re gonna measure it based on some sort of, you know, whatever the business metric is or the key outcome is. And then let’s work on fi, let’s work on figuring it out. And then, then, then, then basically how we, how we do that is okay. We, uh, I think my approach has always been, um, this is what we want to do. This is what we think we need to do to do, uh, do it. And then what are we gonna commit to? Like, when do we think, what are we gonna commit to? When are we gonna get it done, right? And how well do we do to that? Right? So I think that’s how we tie it all together. Um, so basically just yeah, uh, you know, getting us all line aligned on objectives, right? And making sure the objectives have meaning to the team. Like I, I, it’s always hard when people feel like why am I doing this, right? And, and that’s always the worst, right? But if it’s clear that, hey, we, we know how this is gonna make an impact on our customers or the business, and they can see it. Um, and then, but then aligning to, okay, we see it, we see the problem, here’s a solution, we think it’s gonna work. Uh, here’s what we’re committing to, to fix it. And then, then, then it’s really measuring, you know, how well did we meet on committing, getting to that? You know, did we re, did we deliver what we said we’re gonna deliver? Did we deliver it on time? Those are things that we kind of look at.
Kovid Batra: Got it. Got it. What, what do you have to say, Clint? Uh.
Clint Calleja: It’s, uh, it, it’s, uh, my, my definition is very much aligned. Like, uh, from a, a higher perspective. To me, it all boils down. And, um, how well are we, uh, and how well and quickly are we delivering the right value to the user? Right? And, uh, this kind of, uh, if we drill down to this, um, this means like how quickly are we able to experiment and learn from that as our architecture is allowing us to do that as quickly as we want. How well are we planning and executing on those plans, uh, with least disruptions possible, like being proactive rather than be reactive to disruption. So there’s, you know, there’s a whole, uh, sphere of accountability that we, we, we need to take into consideration.
Kovid Batra: Makes sense. I think both of you, uh, are pointing towards the same thing. Fundamentally, if I see it, it’s about more about delivering value to the customer, delivering more value, which aligns with the business goals, and that totally makes sense. But what, what do you guys think, uh, when it comes to, uh, other peers, uh, other engineering leaders, do you see the same perspective, uh, about engineering productivity, uh, Rens?
Rens Methratta: Um, I think in general, yes. I think, I think you end up and I, and I think sometimes you end up getting caught in. Um, I, you know, sometimes you get caught up in just hitting, trying to hit a metric, right? And then losing track of, you know, are we working on the right things? Is this, is this worthwhile? I think that’s when it’s, it could be, uh, you know, maybe problematic, right? So I, you know, I even in my early in my career at this, if they, where I’ve, I’ve done that, like, hey, you know, let’s, let’s be really as efficient as possible in terms of building a metrics organization, right? We’ll do kind of, everything’s our small projects, right? And we’ll get these things in really quickly. And, you know, I I, I think that, you know, what I learned is in that situation, like, yeah, we’re, we’re doing stuff, but then the team’s not as motivated. The, you know, we’re not, it’s not as collaborative, the outcome isn’t gonna be, um, as good. Like I think if we have, I think the really key thing is from my perspective, is keeping having a, a team that’s engaged, right? And being part of the process and proactive. Right. And obviously measuring to what the outcomes are, but, um, that’s side of my, where I feel it’s great when we, when we go to a, like a, a, uh, or a retrospective or a sprint planning where we’re like, Hey, teams are like, I don’t think this works. Like I, I, the worst part is when you get like crickets from people, like, okay, this is what we wanna do. Like, and no, no real feedback. Right? I think I really look for, you know, how engaged teams are, you know, how in terms of solving the problem, right? Um, and that’s, and I think that a lot of that cross collaboration, right? And how, um, and building a, a kind of environment where people feel empowered to kind of ask those questions, be collaborative, ask tough questions too, right? Like, I, I love it when an engineer says this, this is not gonna work, right? And it’s great. I’m like, yeah, let’s tell me why. Right? So I, I think if we can build cultures that way, that, that, that’s, that’s ideal.
Kovid Batra: Makes sense. Perfect. Uh, Clint, for you, uh, do you see the same perspective and how, how things get prioritized in that way?
Clint Calleja: I, I particularly love the focus and the tension on the culture, the cultural aspect. I think there’s, there’s more to it that we can unpack there, but yes, in general, um, I think actually when, when I heard this question, it reminded me of when I realized the needs of data points, not just for the sake of data points, of KPIs, but what I started to see as the company started to grow is that without sitting down and agreeing on what good looks like, what are the data points that we’re looking at, I started to see, um, a close definition, but not, not exact definition, which still left, you know, openness to interpretation. And there were cases as we grew, uh, bigger and bigger where, for example, I felt we were performing well, whereas someone else felt differently. And then you sit down and you realize, okay, this is the crux of the problem, so we need that. That was the eureka moment where like, okay, so this is where we need data points on team performance, on system performance, on product performance in general.
Kovid Batra: Yeah. Makes sense. I think both of, both of you have brought in some really good points and I would like to deep dive into those as we move forward. Uh, but before, like we move on to some specific experiences around those pointers, uh, Clint, uh, in your journey, uh, over the last so many years, there must have been some counterintuitive things that I would have that you would’ve realized now, uh, uh, that are not aligned with what you used to think earlier, how productivity is driven in teams. Are there any, anythings? Is, is that thing ringing a bell?
Clint Calleja: Uh, well, if you ask me about learnings on, uh, you know, things that I used to think are good for productivity and now I think they’re not, and I evolve them, I think I keep having these one and one out, right? Um, but again, like, uh, the alignment on piece, key set of indicators rather than just a few was one of those big changes in my, in my leadership career, because I went from using sprint, um, as the only data points to then extending to understanding why the DORA metrics better, why actually SPACE matters even more because they’re the, um, the, the wellbeing factor and how engaged people are. So, uh, I realized that I needed to form a bigger picture of data points in order to be able, able, able to understand all the picture. And again, not just the data points, the quantitative data points, I also needed to fill in the gaps with the qualitative parts.
Kovid Batra: Sure. I think, yeah, that goes hand in hand, uh, alone, either of those is not sufficient to actually look at what’s going on and how to improve on those factors. Perfect. Makes sense. Rens, for you, uh, there must have been some learnings over the years and, uh, anything that you find was intuitive earlier, but now it’s counterintuitive? Yeah.
Rens Methratta: Yeah, no, I think I, learnings every day. Um, but I, I, yeah, in general, maybe what Clint said, right? So you, I did end up at some point overindexing on like some of the quantitative stuff, right? And it’s like, and, and you lose track of what are you trying to do, right? So, hey, I did, we did really well. We, um, you know, we’re doing, um, in terms of, uh, you know, we, we got through our sprints, we got, we were getting to the, uh, we’re doing planning where, hey, our meeting times are low, right? Or these, these are so many things you can, there’s so many things you can look at, and then you lose pic, then lose track of the greater picture, right? So I, I do think of, you know, identifying those north stars, right? And these was referencing, right? Those what we think are important, saying, Hey, what are, how are we measuring to that? And also then also that helps you make sure you’re looking at the right metrics, potentially, right? And putting them in the right context, right? So, you know, it doesn’t matter your, if your velocity’s great, if you’re not building the right things, right? If you, it doesn’t matter if you’re like, so those are the things I think you kinda learned. Like, hey, sometimes it’s just. Um, you know, simplify, you know, the, you know, the what you want, what you, the what you, what you look at, right? And, and try to, try to think through like a, how are, how are we as a team meeting that? And also try to, primarily from a team perspective, right? Getting alignment on that. Like, Hey, this is what we’re, this is the goal we’re trying to get to. And I, I feel like that’s when you get most, uh, commitment from the team too. Like, Hey, uh, it’s easy. I know what we’re trying to do it, and it, it, it, it motivates people to be like, yep, this is what we’re trying to get to. It’s, it’s, it’s something to celebrate when we get to it, right? Otherwise, it just gets, I, it’s, it’s not hard. It’s, it’s it’s hard to celebrate. Like, oh, we got, we got X velocity. I’m like, ah, that’s not, that’s not right. So yeah, I think that’s a better learning, simplifying, right? And, and, um, and also, you know, simplifying in a way and then defining the metrics based on those core metrics like the, uh, and so they all flow down so that it, it aligns, right? And people, you can point something easily, easily and say, this is why it’s important. Right? Um, yeah, I think that’s really important when you communicate to people, Hey, look, this is problematic. Uh, we need to, we might need to take a look at this. And be able to really simplify, say, this is why it’s important, this is why it’s problematic. Uh, this is why it’s gonna impact our North Star. Right? I think that makes conversations a lot easier.
Kovid Batra: Totally, makes sense guys. I think having the right direction along with whatever you are doing on day-to-day basis as KPIs is very important. And of course, to understand a holistic picture, uh, to understand the developer’s experience, a team’s experience to improve overall productivity, not just quantitative, but qualitative data is equally important. So I think to sum up both of your learnings, I think this is a good piece of insight. Now, um, we will jump onto the next section, section, but before that, I would like to, uh, tell my audience, our audience that uh, if they have any questions, we are gonna have a QnA round at the end of the session. So it’s better you guys put in all your questions in the comments right now so that we have filtered it out by the end of the episode. And, uh, moving on now. So guys, uh. The next section is about specific experiences that we are gonna deep dive into from Rens and Clint’s journeys. Uh, we’ll start with you, Clint. Uh, I think the, the best part about your experience that I have felt after stalking you on LinkedIn is that, uh, you, you have had experience of going through multiple acquisitions and, uh, you work with smaller and larger organizations, high performing teams. Uh, this kind of experience brings a lot of exposure and we would want to learn something from you. How does this transition happen? And in those transitions, what, what should an engineering leader should be doing, uh, to make it more, uh, to not make it overwhelming and to be more productive and do the right things, create the impact even during those transitions?
Clint Calleja: Uh, yes. Uh, we’ve been through a, I’ve been through a couple of interesting experience, and I think like, uh, I dare to say, for me, they were like, especially the acquisition, uh, where HR was acquired was, um, uh, a unique, a very unique experience to big companies merging together. Um, it’s very easy for such a transition to be overwhelming. I mean, there’s a lot of things to do. Um, so I think the first key takeaway for me is like, clear communication, intentional, um, communication, regular, and, uh, making sure that we as a leader, like you’re making yourself available to support everyone and to help to guide others along this journey. Um, it’s, then there’s the other side of it that, you know, uh, it, it, such an experience is. Um, does not come without its own challenges, right? Uh, the outcomes are big. Um, uh, and in engineering leadership specifically, you know, that there’s a primary, um, area that you start to think about is, okay, the, the systems, what does it mean when talk about technology stacks the platforms? But it’s something not to underestimate, is also the ways of working in the culture when merging with companies because, uh, I, I ca, I ca I started to, uh, coming to the realization that I think there’s more effort required than planning there, than in the technology side, um, of the story. So, very interesting experience. Um, how to get the teams up and running. I mean, my experience last year was very, again, very, very challenging in a good way. You know, I started in a completely new. Department with about 55 people. 70% of them were new to me coming from the parent company. So we needed to, we already had goals to deliver by June and by September. So it, yes. Talk about overwhelm. And I think one of the, one of the key, um, exercises that really helped us, um, start to carve out, um, some breathing space was these iterations of higher level estimations of the things that we need, um, to implement. Uh, they started immediately enabling us to understand if we needed to scope, if we needed to have discussions to either delay or the scope or bring more, uh, people to the mix. So, and following that, you know, kickstarting, we needed to give some space to the teams to come together, start forming their ways of working. The same time getting a high level understanding of what we can commit to. And from there it’s all, again, about regular communication and reflections. It’s like, okay, biweekly, let’s have a quick update, um, and let’s call a spa. The spa. If we’re in the red, let’s call it out. I’d rather, you know, we’d rather know early so that we can do something about it where there’s time rather than, I’m not sure if you’ve ever seen the situation, but you see a status in the green for almost all a quarter. All of a sudden you get to the last two weeks and then the red. So.
Kovid Batra: Makes sense. Um, while, while we were, uh, initially talking, you said there is a lot to unpack on the, on the developer experience part. I’m sure, uh, that that’s something very core to you, your leadership style, where you ensure a good, uh, developer experience amongst your team. So now you shifted to a new team, uh, and in general, wherever you have been leading teams, uh, are there any specific practices around building a good developer experience that you have been following and that have been working for you? And if there are, uh, can you just share something?
Clint Calleja: That’s a very good question, because I, I see different teams, right? So I’ve done different exercises with different teams, but in this particular case, where I started from was I, I realized that, okay, when you have a new, uh, line being formed, mixed cultures coming from different companies, I said, okay, the one thing I can start with is at least providing, um, a community culture, um, where people feel safe enough to speak up. Why? Because we have challenging goals. We have a lot of questions. There are areas that are known. If people won’t be able to speak up, will, you know, the probability of surprises is gonna be much, much higher.
Kovid Batra: Right.
Clint Calleja: Um, so what are some elements, you know, some actions that I’ve taken to try and improve here? So I think when it comes to leading teams directly in general, we find much more support because even if you look at the Agile manifesto, it talks about a default team where you have a number of engineers who have a trio, ideally, you know, enabled to do decision making. There’s a pattern of reflections that, uh, happen, as Rens said in the retrospectives. Ideally actions get taken. There’s a continuous cycle of improvement. What I found interesting was that beyond one team, right, when I started to lead other leaders or managers, I could see a much bigger opportunity in this team of leaders or team of managers to actually work together as a team. By default, we’re all kind of more focused on our scope, making sure that our people, you know, are, are, are, are, uh, well supported and, uh, and heard and that our team is delivering. But I, I think it’s also worth thinking about if we’re calling it developer experience, let’s call this the manager experience of how much can we help each other out? How much can we support each other to remember that we’re people before managers, you know, like, uh, I dunno, it’s not the first time I, I went to work where I wasn’t feeling so great. So I needed to fine tune myself, expectations on what to produce. So if this is not shared outside with my, with my lead, with my manager, or with my peers, you know, their expectations cannot adjust accordingly. So there’s, there’s a lot of this that I try to, to prioritize by, uh, simple gestures like sharing my weekly goals, trying to encourage my managers to do the same.
Kovid Batra: Yeah.
Clint Calleja: So we can understand the, we try to take one too much in end of week reflection. Think of it like a retrospective, but between managers to say, okay, hey, it was much more disruption than I anticipated this week, and it’s okay. Part of it is actually the psychological safety of being able to say, oh, my short 400%, I only achieved 50. It’s okay. But I learned, right, and I think in terms of metrics, another exercise that I immediately tried to restart in my new line was this exercise that I call the high altitude flight. And this was an exercise where as leaders, we connect those KPIs, um, with qualitative data, like, uh, the weekly pulse and feedback from 15Five, for example. Mm-hmm. And we talk about it on a monthly basis. We bring those data points on a board, you know, start asynchronous, we raise the right questions, uh, challenge each other out and this way we were regularly bringing those data points into the discussion and making sure we’re prioritizing some actions towards them.
Kovid Batra: Totally. I think, um, after talking to so many engineering leaders, one common pattern that I’ve seen, uh, in some of the best leaders is they, they, they over communicate, like in, in a very positive sense I’m saying this, uh, they, they tend, because everyone feels that, uh, a lot of times you’re in a hybrid culture, in a remote culture where as much as you communicate is less actually. So, having those discussions, giving that psychological safety has always worked out for the teams, and I’m sure your team would be very happy in the way you have been driving things. Uh, but thanks, thanks for sharing this experience. I’ll get back to you. Uh, I have a few questions for Rens also on this note. Uh, so Rens’ journey has also been very interesting. He has been the CTO at Prendio and uh, recently I was talking to him about some of the recent initiatives that he was working on with the team. And he talked about some, uh, copilot and, uh, few other automated, uh, code analysis tools that he has been integrating in the team. So Rens, could you just share some experience from there and how that has, uh, impacted the developer experience and the productivity in your teams?
Rens Methratta: Um, yeah, I’d be happy to. It’s been, I think there’s a lot of, a lot of change, uh, happening in terms of capabilities with, uh, AI, right. And, and how we best utilize it. So like, we’ve definitely seen it, you know, as, as models have gotten better, right, I think the biggest thing is we have a, you know, relatively large code base and um, and newer code base for things. And so it’s, it was always good for like, maybe, maybe even like six months ago we would say, Hey, it’s, we can look at some new code, we can improve it, write some unit tests, things like that. But you know, having an AI that has like a really cohesive understanding of our code base and be able to, um, you know, have it, you know, suggest or build, uh, code that would work well, it wasn’t hap, it wouldn’t happen, right. But now it is, right? So I think that’s, that’s coming, the probably the biggest thing we’ve seen in the last couple months and really changing, um, you know, how we think about development a bit, right? Kind of moving, uh, we’ve done some, you know, a lot of this is AI first development, like it changed mindset for us as a team, right? Like how do we, how do we build it? Um, you know, lots of new tools. I think, Kovid, you mentioned there’s tons of new tools available. Yeah. It’s changing constantly, right? So, um, you know, we’ve spent some time. Looking at, looking at some of the newer tools, um, we’ve actually, uh, agreed to as of now, uh, a tool, we, we actually gonna moved everyone over to Cursor. Right. ’cause I just on terms of, um, like what the capabilities it provided and, and, and that, so, uh, and then similarly outside of code, yeah. It’s like, uh, there are tools that, you know, typo has the, uh, you know, for the pull requests, the, you know, uh, uh, summary, things like that’s really helpful, right, for things of that, even on the, and then automated testing, uh, there’s a bunch of things that I think are really changing how we work and make us more productive. And, and it’s, it’s challenging ’cause it’s, you know, it’s, obviously, and it’s good. It’s, it’s a lot of new stuff, right? And it’s really re-making us rethink how we do it. Like, um, you know, developing. So we built, uh, some things now from an AI-first approach, right? So we have to kind of relearn how we do things, right? We’re thinking things out a bit more, like defining things from a prompt first approach, right? What are our prompts, what are our templates for prompts? Like, it’s, it’s been really interesting to see and good to see. Um, uh, and I think, yeah, it definitely made us, uh, more productive and I think we’ll get more productivity as we kind of embrace the tools, but also kind of embrace the mindset. It’s, um, I think for the folks for who’s actually used it as most, and you can kind of see like when they first start utilizing it to maybe where they’re now, like the productivity increase has been tremendous. So that’s probably the biggest change we’ve seen recently. Um, but it, it’s an exciting time. We’re, we’re looking forward to kinda learning more and, and it’s something that we have to, um, you know, we really have to, um, get a better understanding of, uh. But again, which also like challenges too. I would say that too. Right? So, uh, like previously we had a good understanding of what our velocity would be. I am, I mean, right now it’s a good, maybe a good thing, like our velocity would be better, right? And it’s higher. So like, you know, uh, even engaging effort, things like that, it’s, it’s com, it’s a lot of new things that we’re gonna have to learn and, and figure out and, um, reassess. Um, but, um, but yeah, I, I mean, I, I think if I, if I look at anything that’s been different recently, that’s been probably the biggest thing and the biggest change for us in terms of how we work. And then also in kind of incorporate, making sure that we incorporate that into our existing workflows or existing development, uh, structure. That’s, I think it’s a lot of new changes for our team, um, trying to help, help us do it, uh, effectively and making sure we’re thinking about it, but also like giving our team power to like try new stuff has also been really cool too.
Kovid Batra: Perfect. Perfect. And, uh, my next question is actually to both of you. Um, you both are trying to implement, let’s say, AI, uh, think you’re trying to implement a better communication, better one-on-ones, bringing that psychological safety, everything that you guys do, I’m sure you, you both have some way to measure the impact of that particular initiative. So how do you guys actually, uh, measure the impact of such initiatives or such, uh, let’s say AI tooling that you brought in, bring into the picture. Uh, maybe Clint, you, you can go ahead.
Clint Calleja: I don’t have examples around AI toolings and in general, it’s more, you know, about utilizing the, actually deciding which of those KPIs are we actually optimizing for for this quarter. So I am guessing, for example, in Rens’ case we were talking about how much AI already is influencing, uh, productivity. So, um, so I would, um, assume or expect a pattern of decreased cycle time because of, uh, the quicker time, uh, to, to implement certain code. Um, I think the key part is something that Rens said a while ago is not focusing a lot on the KPI per se, just for the sake of that KP, but connecting it even in the narrative, in the communication, when we set the goal with the teams, connecting it to the user value. So some, for example, some experiences I’ve had. Okay. I had an, an interesting experience where I did exactly that. I just focused on the pickup time without a user connection. And this is where I got the learning. I’m like, okay, maybe I’m optimizing too much about the, the data points. Whereas eventually, we started shifting towards utilizing MTTD, for example, to, uh, reduce the impact of service disruptions on our customers by detecting, um, disruptions internally, uh, using SLOs to understand proactively if we’re eating too much into the other budget. So we actually act before an incident happens, right?
Kovid Batra: Um, right.
Clint Calleja: So, uh, it’s different, uh, different data points. And when going back to the wellbeing, what I found very interesting, I know that there are the engagement surveys that happen every six months to a year usually. Uh, but because of that time frequency, it sets wellbeing as a legging indicator. When we started utilizing, um, 15Five, for example, there are other tools like it, but the intention is, for every one-on-one, weekly or biweekly, you fill a form starting with how well did you feel from 1 to 5. Because we were collecting that data weekly, all of a sudden the wellbeing pulse became a leading indicator, something that I could attribute to a change, an intentional change that we decided to do in leadership.
Kovid Batra: Makes sense. Rens, for you. I think, uh, the question is pretty simple again, uh, like you, you are already using typo, right?
Rens Methratta: We are, yeah.
Kovid Batra: But I would just rephrase my question to ask you like, how do use such tools to, uh, make sure maybe your planning, your execution or your automation or your reflection is in place. Uh, how, how do you leverage that?
Rens Methratta: Yeah, and, and I think, I think it’s, uh, maybe understand the same thing. I think, uh, and aligning those two, you know, what the objectives are, right? So I love, uh, I know primarily the sprint retrospective like it, but not the detail, like more on just, um, as a collective team, we said, Hey, this is what we are trying to accomplish, this, we have a plan to do this. We’ve agreed to, um, hey, this is what we have to get done for, you know, these next couple of weeks to make it right. And then it’s, you know, really having the, all that in one place to see like, uh, we said we’re gonna, we need to get all this stuff done. Um, you know, we, this is how, this is how we did, right? So there’s, for us, there’s multiple tools to put together. We have ticketing with Jira, we have obviously Git to kind of get little controls, but then having all that merged into one place we can easily see like, okay, this is what we committed to, this is what we did. Right? And then, and then if, then basically having, being able to say, okay, I will, well, okay, one, okay, here’s where we are. Do we need to, what do we need to do to kind of, uh, kind of problem solve? Are we behind? What do we, what should we do? Right? Having those discussions is great. And then also being like, okay, well, um, and so then it’s again, can we still meet these goals that we wanna do from an objective perspective? What’s holding us back? I think getting to the point where we can have those conversations easily, right? That’s, that’s what the tools, uh, well, and for Typo, that’s what we really use it for, right? So in, instead of, uh, because it’s the context that all those individual stats provide, right? That’s more important. Uh, and and that context towards how does that, at the end of the day, that aligns to what our overall goal is, right? We have this goal, we’re trying to build this or, uh, change this, right? And for our customers, or because of a reason, uh, and, and being able to see like how we’re doing, uh, to that, right? In a, in a, in a good summary is really, is really, uh, is what we find the most useful and then so we can take action on it, right? Um, otherwise if we might, yeah, sometimes you kind of, you look at all these individual stats and you kind of, you, you lose track of it if you just look at those individually. Kind of. But if you have a holistic view of here’s how we are doing, uh, which, which we use it for, that, that really helps.
Kovid Batra: Perfect. Perfect. Clint, do you have anything to add on to that?
Clint Calleja: Not specifically, not at this stage.
Kovid Batra: Alright, perfect. Uh, I think, uh, thanks, uh, both of you for sharing, uh, your experiences. Now I think it’s time for us to move on to the QnA section. And I already see, uh, we are, uh, flooded with a few questions here and, uh, we’ll take a minutes break right now and, uh, in the meantime I will just pick on the questions that we need to prioritize here. Alright.
All right, so, uh, there are some interesting people here who have come up. Uh, Clint, I’m sure you have already seen the name. Uh, Paulo uh, the, the guest from the last session and one of our common friends, uh, uh, I’ll just pull up his question first. So I think, uh, this question, uh, Clint, you can take up. Uh, engineering productivity is a lot about the relationship with the product. As senior engineering leaders, what does a great product lead look like?
Clint Calleja: Very good question. Uh, hi Paulo. Um, well, I, I, I, I’ve seen a fair share, right? Uh, of, uh, good traits in, in product, product leads. That’s not me, right? No, that’s not you. Um, I, I think what I can speak for is what I, I tend to look for, um, and first and foremost, I tend to look for in a partner, um, uh, so ideally no division, because that division, um, easily, um, gets, uh, downstream, you know. You start to see that division happening in the teams as well. There’s the, secondly is in the alignment of objectives. So, um, I always tend to lean on my product counterpart to understand more about the top priorities of our product goals. And I bring, uh, which would answer some of the questions here, I bring in the picture, uh, the top, um, technical solvency, uh, challenges. In order to sustain those product goals. And this way, uh, we find a balance on how to set up the next set of goals for a quarter or half a year. Right. And we build together a narrative that we can share both upwards and with the rest of our teams. Uh, and another characteristic, yes, is regular, um, is actually the teamwork element. Uh, a while ago I explained the differentiation, the opportunity I’ve seen, uh, amongst team leads or managers to work together as a team as well. I think the way I like to see it is as a leader, you have at least three teams. You have the people that, uh, you work for, that report to you. You have your trio as another team, and then there’s the, um, the managers in the department, the other leaders in the department, which is yet another team. So in the product lead, I do lean on for, uh, one of my teams to be one of my team, uh, peers.
Kovid Batra: Makes sense. Perfect. Alright, uh, moving on, uh, to the next question. Uh, that’s from, uh, Vishal. Uh, what are the, some of, what are some of the best practices or tools you have found to improve your own productivity? Rens, would you like to take that?
Rens Methratta: Uh, sure. Um, I’m trying to think. I, I, there’s a lot of tools obviously. I think, I think at the end of the day, I. Um, more than anything else, I would say communication is the biggest thing, right? I would think for productivity. Yeah. From a team perspective. Like, um, like I’ve, you know, I, I’ve, I I’ve worked in a lot of different, uh, types of places from really large enterprise companies to really small startups. Right? And, and I think the common, the common thing, regardless of, of whatever tools we do is really one, how, how well do we, how well are we connected to what we’re building? How well are we, do we as a team understand what we’re trying to build and the overall objectives, right? And, and, um, I think that just, you know, that itself, uh, more than anything is what drives productivity. So like I, you know, I’ve, uh. I, I think the most productive I’ve ever been is, uh, we, we, I was in a startup. We were, uh, we had this one small attic space in the, in, in this, uh, in our, in our city, in, in Cambridge. There was five of us in one room for like, uh, we were, but we were constantly together communicating. Um, and so, uh, and, and then we had, we had a shared vision, right? So we were able to do a lot of stuff very quickly. Um, I think that, so I think what I look into is some of the tools that maybe help us now. It is challenging, I would say, with everyone being remote, right? Distributed. That is probably one of the biggest challenges I, I have for productivity. Um, so, you know, trying to get everyone together. Video calls are great. We try to make sure everyone goes on video, uh, but like at least, you know, try to get, um, as much of that, um, workflow of thinking through like, uh, being together even though we’re not together as much as possible. I, I think that helps a lot. Um, and tools that..
Kovid Batra: Have you, uh, have you tried those digital office tools like, uh, you are virtually in an office?
Rens Methratta: Yeah, we tried that. Uh, I think it’s okay. Uh, we tried some of the white, the whiteboarding tools as well. Right. It, it’s okay. Yeah. You know, quite, and it’s, it’s honestly, it’s, it’s good. Um, but I, you know, the interesting thing I’ve really found, and I, if possible, even if we, if we met one person live for in, in person, even once, right? Yeah. I feel like the relationship between the teams are so much different. So no matter what, no matter how far away we try, we try to get everyone together at least who wants to meet because it is just, uh, I think even like, um, how people’s expressions are, how they are in real life, it, it is so hard to replicate. Right?
Kovid Batra: Totally. Totally. Yeah.
Rens Methratta: And, uh, and those nuances are important in terms of communication. So, um, and, but you know, outside of that, I mean, yeah, I think whatever the things that I would say are the things that can simplify, uh, objectives, right? Make sure it’s clear, uh, anything that would, uh, make, make that easy and straightforward, uh, I think that’s, that’s the best. And then it’s making sure you have easy ways to talk to each other and communicate with each other to kind of, uh, yeah, keep track of what we were doing.
Kovid Batra: Uh, I could see a big smile on Clint’s face when I talked about this virtual office tool. Is, is there an experience that you would like to share, Clint?
Clint Calleja: Uh, not, not really. Like it was, it was fun to hear the question because I’ve been wondering about it as well, but I have to agree with Rens. I think nothing beats, you know, the change that happens after an in-person meetup.
Kovid Batra: Sure.
Clint Calleja: The, the relationships that get built from there are, take a different, you know, a different go to..
Rens Methratta: It is, it is different. Yeah. I, I don’t know why, but if I’ve met someone in person for, I feel like, I know, I feel like I know ’em at a much deeper level than, uh, even though we’re, you know, uh, on video for a long time. It just, it is a different experience.
Kovid Batra: Totally. I think there is another good question. I, I think you both would relate to it. Have you guys had an experience to, uh, work with the Gen Z developers, uh, recently or, or in the last few years?
Rens Methratta: I, I mean, I probably, I’m trying to think through like what Gen Z would be. Yeah.
Kovid Batra: I, I, I get that in my circle, uh, a lot that dealing with the Gen Z developers is getting a little hard for us. And there’s like almost 10 to 12 years and maybe more age gap there. And the thing, and things have changed drastically. So, uh, people find it a little hard to understand and empathize, uh, on, on that front. So do you have anything to share? By the way, this is a question from Madhurima. Uh, yeah.
Rens Methratta: I think, well, I think in general, I think I will just say maybe not, maybe Gen Z, but just in general for junior, more junior developers we bring on board like younger developers, I think it’s, it has been challenging for them too because I think a lot of it’s been remote, A lot of their experience has been remote, right? Um, I think it is harder to acclimate and, and that that a lot of the stuff I’ve learned when I remember, uh, coming up as a software engineer and that’s, a lot of that experience has been like, you know, getting in, meeting with people, whiteboarding, getting through that, right. And having those relationships, um, was really beneficial. So I definitely think it’s harder, um, in that sense. Uh, I, I do think we’ve, uh, personally tried to try to get, um, you know, people who are more junior developers, you know, try to more opportunities to, um, you know, uh, more coaching, um, uh, and, and also like, uh, more one-on-one time just to try to help them acclimate to that because I think we’ve identified that it is harder, especially if we’re being remote first. Um, I haven’t had any, um, I don’t think anything, yeah, I know the memes of the Gen Z developers. I haven’t got any meme worthy stuff or experiences for Gen Z developer. Hasn’t been that, so I’ve maybe, I’ve been lucky, so, but I, I do, but I would empathize with that. It is harder for junior devs because, you know, we are in a much more, you know, uh, remote world and it, it, it’s harder to make those connections.
Kovid Batra: Totally. All right.
Clint Calleja: I think, uh, if I, if I may add something to this, I, uh, maybe what I, I’d add is I, I don’t have a specific way to deal with Gen Z developers because what I try to do is I try to optimize for inclusivity. Okay, there’s Gen Z, but there are many other, you know, cultures and subcultures that require specific attention. So at the end of the day, what I found to be the, at least the best way forward, is a good, strong set of values that are exemplified, that comes from the company, a consistent way of sharing feedback, uh, and the guidelines of how feedback is shared, and of course, making space for anyone to be heard in their preferred, uh, way, you know, they’d like to communicate and you can easily understand this if, you know, as just a part, part of your onboarding, you ask people to provide the user manual for you to understand how is it the best way, you know, for these people to communicate, to get the feedback. So think of it this way, it’s like, okay, providing the support for interfaces which are consistent for everyone, but then being available, uh, for everyone to communicate and get the support the way they prefer it, if that makes sense.
Kovid Batra: Okay. Totally. Alright, uh, thanks guys. Moving on to the next question. Uh, this is from Gaurav. Uh, how do you balance short-term deliverables with long-term technical debt management? Also, how to plan them out effectively while giving some freedom to the engineering teams, some bandwidth to explore and innovate and delve into the unknowns. Uh, Clint, would you like to go first?
Clint Calleja: Sure. Uh, when I, when going through this question, the first thing that came to mind, something that I wanna be clear, I’m not an expert of, but I started, you know, trying and iterating upon is the definition of an engineering strategy. Uh, because this is exactly what I used to try and understand, get a di.. So there’s this, the, the book, uh, ‘Good Strategy Bad Strategy’. So I try to replicate the tips from there. And it’s basically getting a diagnosis of, okay, where’s the money coming from? What are our product goals? And there are other areas to cover. And then coming up with policies, guiding policies. So the, you know, your team knows the direction we want to go, and some high level actions that could be really and truly could become projects or goals to be set as OKRs, for example, I don’t know. Uh, we realized the need from the diagnosis. We realize the need, we need to simplify our architecture, for example. So then I connect that engineering strategy and actions to goals, so that the teams have enough freedom to choose what to tackle on, uh, first, uh, whilst having enough direction on my end.
Kovid Batra: Makes sense.
Clint Calleja: So I’m still fine tuning on how, how good that strategy is. Right. But it’s, it really helps me there.
Kovid Batra: Perfect. Uh, the other part of the question also mentions about giving engineering teams that bandwidth, uh, that freedom to innovate and delve into the unknown. So, of course, one part of the question does get answered from your strategy framework, but in that, how, how much do you account for the bandwidth that teams would need to innovate and delve into the unknown? Uh..
Rens Methratta: I, I can deal with that or Clint, either way, I, I think..
Clint Calleja: Go, go, go, go.
Rens Methratta: No, uh, it’s, it’s an interesting point. Like, um, we look at it, I, I think in, in general, like I, we define it like an overall architecture. We try to, for everything we do, like here’s our high level where we want to be from a technical perspective, right? And then whatever solutions we’re trying to do, we, we always wanna try to get to that. But there’s always these, you know, the short and long term and, and how much do we give engineers ability to innovate? We really look at it this way there. If it’s something we need to do right away and we say, Hey, look. Uh, and then, um, and typically if someone has a really great idea and then just like, let’s, let’s do it. Uh, I think our overall question is, okay, worst case scenario, what’s our long, how long would this take to, uh, completely redo to get back to our architecture? Right? Um, and if it’s, if it’s like, Hey, if it’s not gonna, it’s, it’s not gonna increase in, it’s not gonna increase in complexity to redo this a year from now if we, if this is the wrong mistake, right? If we, if the, so we, we are much more lenient towards let’s try something, let’s do this, right? If we think worst case scenario, it’s not gonna be exponentially worse if we put this into production to, to roll this back. Right. And so, uh, if it’s something that is gonna say like, oh, this is gonna lead us down a path where if we’re, this is gonna be, we’re never gonna be able to be fix this, right? Or it’s gonna take us so much effort to fix this, then we’re much more careful and we’re like, well, let’s, let’s see, you know, we might not wanna give as much leeway there. So that’s, that’s kinda how we balance it out typically.
Kovid Batra: Makes sense. Makes sense. Perfect. Uh, moving on, uh, probably the last question. Uh, this is from Moshiour. Uh, what’s your approach to balancing new feature development with improving system? I think this is what we have already taken up. Do you have practical guidelines for deciding on when to prioritize innovation versus strengthening your foundations? Uh, Moshiour, I think we just answered this question, uh, in the previous question. So, we’ll, we’ll, uh, give this a pass for now, uh, and move on to the next question. Okay. There is another one from Paulo. Uh, how much of engineering productivity do you attribute to great engineers versus how work and information flows among individuals? So, Rens, would you like to take that?
Rens Methratta: Um, this is like a yes and yes. Like, I mean, uh, I, I, I think, uh, really great engineers have like, you know, really great productivity, right? It’s, it’s, it’s, it’s a both, it’s a both thing, right? So if you have, um, we’ve seen, I think we’ve kind of seen it from, I get more experienced, like, uh, even on the let’s recent stuff on the AI side. Like we, we playing around with folks who’ve really have gotten understand, understood our, like really solid understanding of our technical infrastructure, but can, you know, learn to use those tools effectively. The output is, is like maybe 10x, but someone who’s, um, you know, not as solid on like maybe some of our existing code base technical understandings and utilizing it is, is still improving. It’s like, you know, maybe 2x, 3x, right? So you definitely see that difference. Um, and I think that’s important. Um, but I, I think, you know, the other part about that is communication between the teams and how you do it and making sure that, similarly going back to productivity, like are we, are we building the right things? Right? We can build, yeah, you know, a lot of, a lot of stuff very quickly, but it might not be worth it if we don’t communicate well, we’re probably building completely different things. So I, I think it goes hand in hand. Um, I, you know, I think, I don’t think there’s a really way to. Uh, it’s not an, it’s not an ‘or’, it’s really an ‘and’.
Kovid Batra: Perfect. No, I think it’s, it’s well answered. Clint, do you have anything to add here?
Clint Calleja: It’s, uh, very much in line with Rens, I think, and even, you know, even in fact the KPI, the KPI suggest looking at the holistic of a theme. So once I do believed that, you know, great engineers, the experience an engineer brings will make a difference. It’s not the first time I’ve also seen great engineers not compatible with a team, and they, you know, the, the, it doesn’t work out. So you start to see that the productivity is not really, uh, improving. So yes, you need great engineers, but, uh, there’s a very big emphasis. I think it goes, it’s beyond 50/50. I think there’s a bigger emphasis, in my opinion, on the ways of working, the respectful ways of working, small details. I don’t know, like, um, when is, when should I expect my teammate to pick up a pull request during the sprint? Um, how do I make it easier for them? Should opening up a request with 50 change files, embedding refactoring with a bot fix, does that make it, you know, small things. But I think this is where, um, you can reduce a lot of friction and may make, uh, bring more harmony.
Kovid Batra: Okay. Makes sense. Um, you guys, I think we are already, uh, done with our time today, but, uh, I feel bad for other people who have put in questions, so I just wanna take one more, uh, this sounds interesting. Uh, are you guys okay to extend it for like 2–3 more minutes?
Rens Methratta: Sure.
Kovid Batra: Perfect. Uh, this question comes from Nisha. Uh, how to align teams to respond to developer surveys and use engineering metrics to improve overall experience and performance. So I think both of you have some experience here. Uh, clint is, uh, already, uh, a promoter of having communication, having those one-on-ones with teams. So, and for, uh, Rens, I know he’s using Typo, so he’s already into that setup where he is using engineering metrics and developer surveys with the, with the developers. So both of your opinion would be great here. Uh, Rens, would you like to go first?
Rens Methratta: Um, yeah. To Nisha’s question, um, I’ve never had good luck with like, surveys and, uh, with like developers, quite honestly. They’re just not, um, you know, I think a lot of it is, uh, time spent and, and, and, you know, I, I try to try to do one-on-ones with people, um, and just, you know, get an understanding of how people are doing. Um, I, I, you know, um, we’ve done, tried to do surveys and it’s, you know, people, it becomes, people aren’t, you know, I don’t think the, the responses get, um, less and less valid in some ways if, if it becomes robotic, uh, in a lot of ways. So I, I really think, I think aligning to how people are doing is, from my perspective, is really more, more hands-on, more one-on-one discussions and conversations.
Kovid Batra: Makes sense. How, how did that work for you, Clint? Uh..
Clint Calleja: I, uh, what, what, what Rens just, uh, just, just explained, uh, resonates with a lot of my experiences in the past. It was, uh, a different and eye-opening experience at Hotjar, where I’ve seen the use of, the weekly use of such a survey being well, um, being, um, well adopted. And when I joined Hotjar, I joined as an individual contributor, as a front end engineer. So the first time I had to fill one of these, first I was like, okay, I have to do this every week. But the thing that made me change my mind was the actions I was seeing coming out, the benefits for me that I was seeing coming out from my lead. This wasn’t just a form, this was becoming the talking points of the one hour session I had with him every week. Actions get taken out, which were dedicated to me. So it was a fun fact. This was the first remote experience for me, but the one-on-ones felt like the most tailored I’ve ever had. So think..
Kovid Batra: That’s interesting. Yeah.
Clint Calleja: If I can sum up on the developer surveys, um, I understand that the less people can under an attribute, their input to actual outcomes, to actual change then, you know, why spend the effort? So on, on my end, what I try to do as much as possible is not just collect the data. Here’s a summary of the points. Here are some actions which are now part of this strategy. Remember the connection of the strategy. And here’s why when we are trying to attack what. So again, not a silver, uh, silver bullet.
Kovid Batra: Yeah. Yeah.
Clint Calleja: And then the second part on engineering metrics, I think here, uh, I really rely on engineering leaders to be the glue of bringing those data points into the retrospectives. So the engineering managers are in the best position to connect those data points with the ways of working and the patterns seen throughout the sprints. And in an end of sprint review, you know, express, okay, here are the patterns that I see. Let’s talk about this. Let’s celebrate this because it’s a, you know, huge milestone.
Kovid Batra: Makes sense. Great. Uh, Rens, you wanna add something?
Rens Methratta: No, I, I, I would agree. I think that’s a good, I that’s a good call out. Uh, yeah. Getting, maybe having more action oriented from the surveys would provide different results. Um, and we, we, we tried something where we try to do our, do our one-on-ones as a, as, as a daily survey. Yeah. I didn’t think it was successful because it, it didn’t, I think people weren’t, um, weren’t seeing that individual response back from them. Right. It was just more like data collection for data aggregation purposes. Yeah. Wasn’t, which wasn’t, people didn’t seem to value it.
Kovid Batra: Perfect. Perfect. Thank you so much guys. Uh, this was an amazing session. Uh, thank you for your time. Thank you for sharing all your thoughts. It’s always a pleasure to talk to you, to talk to folks like you who are open, take out time from their busy schedules and give it for the community. Thanks once again.
Clint Calleja: Thanks for the invite. Yeah. And nice to meet you guys.
Rens Methratta: Same here, Clint.
Kovid Batra: All right, guys. That’s our time. Signing off for today. Bye-bye. Okay.
How do you transition from being a strong Engineering Manager to an effective VP of Engineering? What challenges do leaders face as they scale their impact from team execution to organizational strategy?
In this episode of the groCTO Podcast, host Kovid Batra speaks with C S Sriram, VP of Engineering at Betterworks, about his career journey from an engineering manager to a VP role. He shares the hard-earned lessons, leadership principles, and mindset shifts that helped him navigate this transition.
✅ From IC to Leadership: How Sriram overcame early challenges as a new engineering manager and grew into an executive role.
✅ Building a High-Performing Engineering Culture: The principles and frameworks he uses to drive accountability, innovation, and efficiency.
✅ Balancing Business Goals & Technical Excellence: Strategies to prioritize impact, make trade-offs, and maintain quality at scale.
✅ The Role of Mentorship & Coaching: How investing in people accelerates engineering success.
✅ Scaling Leadership with Dashboards & Skip-Level 1:1s: How structured communication helps VPs and Directors manage growing teams effectively.
✅ Closing with Inspiration: Sriram shares a poem he wrote, reflecting on the inner strength and vision required to succeed in leadership.
Kovid Batra: Hi everyone, this is Kovid, back with another episode of groCTO by Typo. Today with us, we have a very special guest. He's VP of Engineering at Betterworks, comes with 20+ years of engineering and leadership experience. Welcome to the show, Sriram.
C S Sriram: Thanks. Thanks so much for having me over, Kovid, and thanks for the opportunity. I really appreciate it.
Kovid Batra: No, it's our pleasure. So, Sriram, uh, today, I think we have a lot to talk about, about your engineering and leadership experience, your journey from an engineering manager to engineering leader. But before we get started on that, there is a small ritual that we follow on this podcast. To know you a little more, we would like to ask you one question. Tell us something about yourself from your childhood, from your teenage that defines you, who you are today. So you have to share something from the past, so that we get to know the real Sriram.
C S Sriram: Sure. Yes. Uh, uh, I think the one thing that I can recall is something that happened when I was in my seventh standard. My then school principal, her name is Mrs. Anjana Rajsekar. I'm still in touch with her. She's a big inspiration for me. She founded and she was running the school that I was studying in. She nudged me towards two things which I think have defined my life. The first thing that she nudged me towards was computers. Until then I hadn't really touched a real computer. That school was the first place where I wrote my very first logo and basic programs. Uh, so that was the first thing. And the second thing that she nudged me towards was just writing in general. And that gave me an interest towards, uh, languages, towards, uh, writing, reading, uh, poetry, short stories, novels, all of that. I think that she kind of created those two very crucial parts of my identity and that's what I would like to share.
Kovid Batra: That's really inspiring actually. Teachers are always great in that sense. Uh, and I think you had one, so I really appreciate that. Thanks for sharing. And, Sriram, is there anything from your writing piece that you would like to share with us? Anything that you find really interesting or that you wrote sometime in the past, which you think would be good to share here?
C S Sriram: Oh, I wasn't prepared for that. Uh..
Kovid Batra: No, that's fine.
C S Sriram: Maybe, maybe towards the end. I'll try and see if I can find something towards the end.
Kovid Batra: Sure, no problem. All right. So getting started with the main section, just to iterate this again, we are going to talk about your engineering leadership journey, specifically from an Engineering Manager to a VP of Engineering at Betterworks. I think the landscape changes, the perspective changes, and there are a lot of aspiring engineering managers engineering leaders who are actually looking towards that career path. So I think this podcast would be really helpful for them to learn and to understand what exactly needs to be there in a person to go through that journey and what challenges, what opportunities come on the way, how to tackle them. So, to start with, I think tell us about your first engineering management experience when you moved in, uh, from, uh, from, uh, let's say a tech lead or an individual contributor role to an EM role and how things changed at that point. How was that experience for you? Was that overwhelming or that came in very easily to you and you were there when you, when you actually arrived in that particular role or responsibility?
C S Sriram: I was a programmer once. So I'll start from index 0 instead of index 1. So I had a, uh, index 0 programmer, uh, engineering management experience where I was given the designation of Engineering Manager for about a month. And I ran back to my CEO and said that I'm not doing management. Uh, take the designation away from me, take the people away from me. I'm not doing it anymore. Uh, that was the index 0 and index 1 was when I started my own software consultancy, roughly about 10 years ago.
Kovid Batra: Okay.
C S Sriram: And then I didn't realize I would have to do management. I just wanted that thrill of running my own business. I guess to paraphrase Shakespeare, you know, "Some people are born managers. Some people are made managers. Some people have management thrust on them." So it was thrust on me. It was my necessity that I got into management and for the first five years, I really messed it up. Because I was running a business, I was also trying to get some coding done for the business. I was also trying to win sales. I was trying to manage the people, recruit them and all of it. I didn't do a great job of it at all. And then when I joined Betterworks was where I think I really did something meaningful with, uh, engineering management. I took the time to study some first principles, understood where I went wrong and corrected. So yeah, that's how I got into management. And it was, uh, it wasn't scary the first time because I didn't know I was doing it. Uh, so I didn't know I was doing a lot of things wrong, so there was no fear there. Uh, but the second time around, when I started in Betterworks, I was very scared that, uh, of a lot of things. There were a lot of insecurities. The fact that I was letting go of control and most of the time intentionally, that was a very scary thing. But yeah, it's, it's comfortable at the moment.
Kovid Batra: Perfect. Perfect. But I'm sure that experience of running a business would have brought a lot of aspects which you could have not learned if you were in a trivial journey of a job where you were a software engineer and then moved into, let's say a tech lead or a management role. I'm sure that piece of your entrepreneurship would have taught you a lot more about bringing more value or bringing more business aspect to the engineering. Was it so?
C S Sriram: A 100% yes. I think the main thing that I learned through that was that software doesn't exist in isolation. A team doesn't exist in isolation. You building the most beautiful user experience or design, you building the most beautiful software, uh, most beautiful piece of code that you've ever written, uh, means nothing if it doesn't generate some sort of business value. I think that was the biggest lesson that I took away from that, because we did a lot of work that I would call as very good engineering work, but extremely poor from the business side. I understood that importance that, you know, it is, it always has to be connected to some business outcome.
Kovid Batra: Great. I think there must be some good examples, some real life examples that you would like to share from your engineering management stint that might revolve around having a good culture, that might revolve around building more or better processes in the team. So could you share something from your start of the journey or maybe something that you're doing today?
C S Sriram: Definitely. Yes, I can. I think I'll start with, uh, the Betterworks/Hyphen journey. So when I joined, it was called Hyphen. We were an employee engagement, uh, SaaS platform. We had a team of really talented engineers and a very capable, uh, Director of Product, uh, and an inspirational CEO. All the ingredients were there to deliver success. But when I joined the team, they hadn't completed even a single story. Forget about a feature, a complete, uh, you know, product; they hadn't completed, uh, a single story in over two quarters. What I had to do in that case was just prioritize shipping over everything else. Like there were a lot of distractions, right? The team was talking about a lot of things. There was recruitment. There was the team culture, process, et cetera, et cetera. I think the first thing that I did there was after a month of observation, I decided that, okay, sprint one, somebody has to ship some things. And just setting that one finish line that people have to cross, that built up the momentum that was required, uh, and it kept pushing things forward. And I got, uh, hands-on in a way that I wouldn't have got hands-on before. Like usually I would've jumped into the code and started writing code myself. That was my usual approach until then. This time I got hands-on on the product side. Uh, I worked with the, uh, director of the product, uh, to prioritize the stories, to refine acceptance criteria, uh, give a sprint goal and then tell everybody that, okay, this is the goal. This is what is included. This is what is not included. Get it done. And it happened. Uh, so that's how that got started.
Kovid Batra: Perfect. So I think when you're sharing this, this is from your initial phase when you actually start working as an Engineering Manager and working directly with the product, uh, managing the team, uh, getting into that real engineering management role, bridging that gap. What exactly led you or made you understand that priority? Like, you went in, saw a lot of things distracting you, people and culture changes. So, initially when you moved into such a space, which is completely new, right? What exactly made you realize, okay, one thing is of course, they didn't ship anything for, let's say a good amount of time, so you had to prioritize that and you went in with that goal. But if you just focus on one thing, do not take people along, there is a lot of resistance that you get. So when you were deciding to do this, uh, you cannot be ruthless when you are joining in new. So was there any friction? How did you deal with it? How did you bring everyone on the same page? Is there anything specific you would like to share from that part?
C S Sriram: Yeah, yeah. See, the diagnosis was actually pretty straightforward because I had a very supportive CEO at that time. Orno, that was his name. So he was very supportive. When I told him that, okay, I'm going to take a month to just observe. Don't expect any changes from me. Uh, in the first month, uh, I don't want to just start applying changes. He was very supportive of that, and I was given a month to just observe and make my own notes. Once I diagnosed the problem, the application of solution took a bit of time. The first thing was to build culture. Uh, now a lot of people talk a lot of things about, uh, culture. Uh, to me, or what culture means is what are the negotiable and non-negotiable policies within your team? Uh, like what is acceptable? What is not acceptable? Uh, and even in acceptable, what are the gray areas? That there may be some areas where you have a bit of negotiation that is allowed. Uh, so that was the first thing that I wanted to sort out. The way I did that first was, like I said, I spent a month studying the team and then I proposed a set of working rules. I talked about working hours. Uh, that was the time when we were all in office. So presence in, uh, office, the work, how do we do work handoff? How do we make decisions? All of those things. Uh, and these, uh, I presented some of them saying that, see, I am tasked with getting some things done. So these are non-negotiable for me. Uh, like you are doing this, uh, you don't have the space to negotiate and say that you are not going to be in office for two weeks, for example. Or you're not going to say that, uh, I won't write automated tests. Those are my, uh, you know, addition areas. I'm owning them. But you can say that, uh, I will be 10 to 15 minutes late because of Bangalore traffic. So we had that kind of agreement that was made and we had an open discussion about it. That was the first presentation that I made to the team saying that these are our working rules and this is how we'll proceed. And I need explicit agreement from all of you. If anybody is not going to agree, you let me know, we'll negotiate and we'll see where we can get to. Now, once that happened, uh, there was a question of enforcing the policy. And I think this is where I failed in my previous attempt at management. I had a set of policies, but I wasn't very consistent in enforcing them. And this time I had a system where I said that, okay, if someone strayed from a policy, someone said that they'll do something, but they haven't done it, my usual reaction would have been either if I thought it wasn't so important, ignore it. Or if it was important, you know, just go ballistic, go lose your temper and ask questions and, uh, you know, do that boss kind of stuff. This time I took a different approach, which was curiosity over trying to being, uh, you know, trying to be right. So I spent a bit of time to understand why did, you know, this miss happen? Why did this person stray from the agreed policy? Was it because the policy itself wasn't well-defined? Uh, or did they agree to the policy without fully understanding it? Or was it just a, you know, human error that can be corrected? Or is it an attitude issue that I can't tolerate? Now in most cases, what happened is once I started putting these curious questions and I started sharing them, people started aligning themselves automatically because nobody wants to be in that uncomfortable position of having to explain themselves. It's just human nature to, you know, avoid that and correct themselves. So that itself gave me the results most of the time. In a few cases, uh, the policy wasn't well-defined or it wasn't well-understood, in which case I had to refine it and make sure it is explained very clearly. And the last thing was, uh, in a few cases where despite repeated feedback, they couldn't really correct themselves. I had to make the decision saying that, okay, this person is not suited for what I want and I'll have to let them go. And we've made some decisions like that also.
Kovid Batra: I think setting those ground rules becomes very important because when you go out and just explicitly do something, assuming that, okay, this is, uh, this is something that should be followed and people are not aligned on that, that creates more friction than, uh, if they're beforehand aware of what needs to be done, how need, how it needs to be done. So I think stepping into that role and taking up that responsibility, it's a good start to first diagnose, understand what's there, and then setting some ground rules with negotiables and non-negotiables. I think it makes a lot of sense. And when you're sharing those specific details, it all the way more aligns with my thought of how one should go out and take up this responsibility. But Sriram, uh, when you jump into that role there are a lot of things that come into your mind that you need to do as an Engineering Manager. What are those top 3-4 things that you think you need to consistently deliver on? I mean, this could be something very simple, related to how fast your teams are shipping. It could be something related to what's the quality of the work that is coming out. So, anything. But, in your scenario, what were your business priorities? According to that, you as an engineering manager, what were your KPIs or what were those things that you mostly aligned with and tried to deliver consistently?
C S Sriram: Yeah, so two things mattered most. And I think it still matters even today for me. The first is what business value is a team delivering. A lot of people get confused where they say they have high-performing teams when actually the teams are just shipping features very regularly, uh, instead of creating business value, uh, like, that's something that I ask my managers a lot as well. Like, what is the business problem that your team is solving? Not just what is the feature that they are shipping next? So that is the first thing. So, um, having a very clear sprint goal, if you're doing a sprint goal, a quarterly goal that says that this is the business outcome that we are achieving. Maybe you're trying to increase the signups. Maybe you're trying to increase the revenue. You're trying to increase the retention. You're trying to solve a specific problem for a customer. A customer is struggling with a particular business outcome at their end, and that is what your software is solving. And once you set, set that as the priority, then adjusting your scope, adjusting what you want to deliver to meet that outcome becomes very easier, very easy. Like I've seen cases where we thought we will have to deliver like 10 or 15 use cases for a feature, but narrowing it down to five, uh, gave us more results because we've been solving what was most valuable for the customer rather than shipping everything that we thought we have to ship. So that is one of the biggest metrics that I try to use. Like, what final business outcome can I connect this team's output to?
Kovid Batra: Makes sense. Almost every day we deal with this situation when, so when I say 'we,' people who are into those position where they have to take some decisions that would impact the business directly. Of course, a developer also writes code and it impacts the business. But I hope you understand where I'm coming from. Like you are in that position where you're taking decisions and you are managing the team as well. So there is a lot of hustle bustle going on on a day-to-day basis. How did you make space for doing this? Uh, that prioritizing even more, highlighting those five things out of those 15 that needs to be done. What kind of drive you need or what kind of process setting you need for yourself to come to that point? Because I strongly believe I have talked to so many engineering leaders and engineering managers, this one quality has always stood out in all high-performing, uh, engineering leaders and engineering managers. They value the value delivery. Like, anything else comes second. They are so focused on delivering value, which makes sense, but how do you make that space? How do you stay focused on that part?
C S Sriram: Uh, see, I think anybody who makes a transition to management from engineering has a big advantage over there. If you are a good engineer you would have learned to define the problem well before you solve it. Uh, you would have learned to design systems. You would have learned to visualize you know, the problem and the solution before you even implement it. Like, a good engineer is going to draw a high-level and a low-level system diagram before they write the first line of code. They will write tests before they write the first line of code. It is just about transposing that into management. This means that before your team starts working on anything crucial, you spend that focus time, and that's where I think a lot of engineering managers get confused as well. I see a lot of engineering managers talking about, Oh, I'm always in meetings. Uh, I don't know what to do. I'm always running around. Uh, having that focus time for yourself, where you are in deep work, trying to define a problem and to define its solution, that makes a huge difference. And when people try to define a problem, I think it always helps to use some sort of standard frameworks. Like right now, uh, as an engineering leader, most of my problem definitions are strategy definitions. Uh, like what policies you know, should the team pursue for the next one to two quarters? What policies drive things like recruitment, uh, promotion, compensation, management, et cetera, et cetera? Now I try to follow some sort of framework. Like I try to follow a policy diagnosis, risk and actions framework. That is how I define my you know, uh, policies. And for each of those problems that you're trying to define, there are usually standard frameworks that are available so that you don't have to break your head trying to come up with some way of defining them. I think leaning on that sort of structure helps as well.
Kovid Batra: Got it.
C S Sriram: And over time, that structure itself becomes reusable. You will tweak it. You will see that some parts of the structure are useful, some parts are not, and it gets better over time.
Kovid Batra: Makes sense. For an engineering manager, I think these are some really good lessons and coming with specific examples that you have taken, I think it becomes even more valuable. One thing that I want to always understand, how much you prioritize quality over fast shipping or fast shipping over quality?
C S Sriram: Yeah. Uh, okay. So I had, uh, an ex, uh, manager who is my current mentor as well, and he keeps saying that he says that 'slow is smooth and smooth is fast.'
Kovid Batra: Yeah, yeah.
C S Sriram: Okay, so I don't aim for just shipping things fast, but I aim to create systems that enable both speed and quality. I think a lot of engineering managers, they always try to improve immediate speed and that's almost an impossibility. Like you can't fix a pipeline while things are running through it already, uh, you need to step away from the pipeline and you're going to get speed results, you know, speed outcomes. Over time, quality outcomes over time. I think that is the first step towards speed and quality. You need to accept that any improvement will take a little bit of time. Now, once you accept that, then defining these things also, again, makes a huge difference. If it's speed, what is speed for you? Is it just shipping features out or is it creating value faster? The best way of increasing speed I've seen is just measuring team cycle time. Like you don't even have to put in any solutions in place, just measuring and reporting the cycle time to the team automatically starts moving things forward because nobody likes to see that it takes two weeks to move a ticket to 'done' in there. And people start getting curious and they start finding out, okay, I'm not moving that fast. I'm actually working a lot more than at that speed, but I've moved only one ticket in two weeks. That's not acceptable. Then you see things changing over there. Same thing with quality also. I like to define what quality clearly means. Like what is a P0, P1 test case that you cannot afford to miss? What are acceptable non-functional requirements? Like, well, you know, uh, not every team has to build the most performant solution. There may be a team that might say that, okay, a one second latency is acceptable for us. A hundred requests per second throughput is more than sufficient for us. So building with that in mind also makes a huge difference. And once you do that, for quality, I would always say the best thing to do is to shift quality left. The earlier you enforce quality in your process, the better it is. And there are standard techniques to do that. You can use mind maps, you can use the three Amigo calls, automated tests, et cetera, et cetera. One example that I can think of is that when I was working with Hyphen, uh, there were a set of data reporting screens, a set of reports which all had very similar kind of charts, grouping and filters. So I spent time with QA to develop some mind maps where we listed all the use cases for all the reports, that were common to all the reports. And we kind of had these mind maps put up during these print review calls during the QA review calls and all of it. If a developer is going to start development, they have it on their screen before they start developing. The developer develops to match those quality requirements rather than trying to catch up with the quality later on. Uh, and this is another example that I like, uh, analogy that I like using as well. Developers, when they write code, they should write as if they are writing an exam where the answers are already available to you and you should really try to score the highest marks possible. Uh, no need to keep anything secret or anything. I think that's an approach that testers should also adopt. You write the exam with every answer available and you score the maximum marks.
Kovid Batra: Makes sense. So I think in your EM journey, if you have to sum it up for us, when was the point when you felt that, okay, uh, you're doing good and these are the top 2-3 things which you did as an EM that really made you that visible, made you that accomplished in a team that you were ready for the next role?
C S Sriram: Got it. I think it took me about a year at Hyphen. So that would be about six years after I started engineering management. So 1 in 5 years running my own consultancy and then 1 year at Hyphen. the outcome that made me feel that okay, I've done something with engineering management was that we ship the entire product. It was a migration from JavaScript to TypeScript, from an old UI to a new UI, a complete migration of a product that was already in use. We hit $2 million ARR and we got acquired by Betterworks. So those were good, uh, you know, outcomes that I could actually claim as victory for myself and for the team. And that was, uh, what I thought was success at that time. But what really feels like success right now is that engineers from that time call me and tell me that you know, working with me during that time was really good and they are yet to find that kind of culture, that kind of clarity. So that is, you know, that turned out to be a good success.
Kovid Batra: Makes sense. Okay, so now moving from that point of engineering management to a leader, how has your perspective changed? I think the company altogether changed because now you are part of Betterworks, which is a bigger organization. You're working with global teams who are situated across different countries. How your perspective, how your approach to the overall delivery, value delivery has changed, I would like to hear that.
C S Sriram: Yeah. So, Betterworks, I would split it into two halves, two and a half years, two and a half years, uh, you know, at Betterworks, uh, leaving that first year at Hyphen. The first two and a half years I was working towards more of a directorship kind of role where I wanted to own complete execution. That was a time I learned how to manage managers, how to get a few other things done as well, like, uh, tie that, uh, you know, the engineering teams outcome, uh, output to the business outcome. The first principle that I learned through that, uh, and the second two and a half years was really about strategy, about executive management. Now, the first principle that I learned that was your first team changes once you start getting on this journey. Until you're an engineering manager, the team that you manage is your team. You belong to that team. That's kind of the outcome that you always look at. Once you start this journey towards engineering leader, that is not your first team anymore. Your first team is the team that you work with, which is your Co-Directors, Co-VPs, your, you know, immediate boss. That leadership team is the core team. You're creating value for that team. And the team that you manage is a "tool" that you use to get those results. Uh, and I would, you know, put a quotation mark around the "tool" because you still need to be respectful and empathetic towards people. It's not just using them, but that's, that's kind of the mindset that you need to adopt. The side effects of this mindset is that you have to learn to be alone, right? At least when I was an Engineering Manager and all of it, uh, there were these moments when you could gossip and complain about what's happening and all of it, the higher up you go, the lesser, uh, you know, you have space for all of that. Um, uh, you, like, who can you go and complain when you have all the power to, you know, do anything. You have the power to do everything that you want. So you have to learn to be alone and to operate by yourself. So that is the second side effect of that. The next principle that I learned was to give up what you take or built. Luckily, it came on, came easily to me at that point. I'm really thankful for that. Like I had built this whole product and, you know, we completed the migration and we got acquired by Betterworks and all of it was something that I was really proud off. But the moment the first opportunity came, I delegated it to someone else. Now, if I had held on to that product because it was my baby, I wouldn't have had the opportunity to scale Betterworks India. We went from I think around five or six engineers, today we are almost 45+, uh, engineers in India. That sort of a 5x scale, 7x scale would have been very difficult to achieve if I had held on to any of the babies that I was building at that time. So that sort of giving up things, uh, is something that's very important. And the next thing that I learned was to coach engineering managers. You basically have to repeat what you did with your developers. Like with, once you manage developers, you don't develop. You delegate. You try to ask them questions. You nudge them and you guide them. You need to repeat the same process with managers as well. That's another thing that I had to learn. And the last thing that I had to learn was setting up teams for success. This was a big challenge because most of my managers were first-time managers at that time. So the potential for failure was huge. So I had to take my time to make sure I set boundaries within which they can make mistakes, fail, and learn. And that was a balance because I couldn't set boundaries that were so safe that they'll never make a mistake.
Kovid Batra: Yeah, that makes sense.
C S Sriram: And at the same time, I, yeah, yeah.. Because it has to be that space. I think you know that, uh. And at the same time, the boundaries can't be so open that they can, they make mistakes that can turn into disasters. And luckily I had good leaders at Betterworks, uh, who guided me through that. So that worked very well. And I also had to spend a lot of time sharing these success stories and learnings with peers and with leadership. Uh, that was something that I didn't invest a lot of time in as a manager. That sort of story building, narrative building both within the team and outside the team, that was another skill that I had to learn.
Kovid Batra: Perfect. So when you talk about the story building and bringing up those stories to your team, which is the leadership, what exactly would you tell them, can you give some example? Like in, for someone who's listening to you right now, what kind of situations, and how those situations should be portrayed to the leadership team would bring a better visibility of your work as an engineering director to the overall leadership?
C S Sriram: Sure. Yes. I think a classic example would be compensation. So I can go back to that just around the COVID time where suddenly investment was booming. The job market was booming. Every candidate that we were trying to hire had three to four offers. We were not assured of the candidate joining us even after they came on board and people were coaching our engineers left, right, and center as well. So that was a crazy time. Betterworks is a very prudent business. That's something that I'm always thankful for. We don't go and spend money like water just because we've got investment. And this means that now as an Engineering Manager, if I'm going to go and talk about compensation, about business planning and all of it with my leadership team, most of the time, I'm just going to say that, hey, this person is demanding so much. That person is demanding so much. I don't know what to do. That is an Engineering Manager approach, and it is justified because an Engineering Manager, depending on what sort of company and what sort of scale you are in has limited scope on what they can actually do in these cases. But the story that you take as an engineering director is you spend time collecting data from the market to see what is the market compensation rate. You see how many exits have happened in your team. How many of those exits are because of compensation, what percentages have those people been offered outside in the market. You collect all that data. You can't even stop at saying that, okay, I'll put all this data in front of management and I'll tell them that see, we are losing people because we are not able to match requirements. We need to change our, uh, you know, numbers. Even that is not sufficient because that is still a director-level, uh, you know, solution that you can offer. If you want to offer a truly executive-level, you are going to look at costs in the business. You're going to look at optimizations that you can do. You're going to come up with a system saying that this is how compensation can be managed. Again, most of the stories that I tell to my executive team come to the point where it's like, there is a problem there is potential solutions, and usually I even recommend one solution out of the solutions that I'm already suggesting. Uh, and this really helps the leadership team as well, because when I think of my boss or my CEO, they are possibly dealing with 20 things that are more complex than I've ever seen in my life.
Kovid Batra: Right.
C S Sriram: So how can I ensure that A, I get the decision that I think is right. And at the same time, I give them enough information so that they can correct me if my decision is wrong. Uh, both are crucial. You know, one of the scariest things that can happen to me is that I get a decision that I want and the decision turns out to be wrong. So giving myself..
Kovid Batra: That's a balanced approach where you are giving the person an option, an opportunity to at least make your decision even better if it is possible and if you're missing out on something. So that totally makes sense. And putting out things to the leadership in such way and how you're solving them would be really good. But one thing that I could understand from your EM to an EL transition you start becoming more cost and budget kind of things being, start coming in more as compared to an EM position. Is it right?
C S Sriram: 100% yes. That's what I've seen with all the great engineering leaders that I've worked with as well. Yes, they love engineering. They get into, uh, engineering, architecture and development at whatever, all levels of interest and time that they have. But there is always a question of how much value am I getting for the money that I'm spending? And I think that is a question that any manager who wants to become a leader should learn to ask like, uh, I think about two and a half years ago when I was asking my then manager, how do I get into leadership? That was the first thing that he said, "Follow the money. Try to understand how the business works. Try to understand where sales comes from. Try to understand where outflow goes." That made a huge difference.
Kovid Batra: Totally. Makes sense. I think this is something that you realize more when you get into this position. But going back to an EM role also, if you start seeing that picture and you emphasize more on that part, automatically your visibility, the kind of work that you're doing becomes even better. Like you're able to deliver what business is asking. So, totally agree. But one thing always surprises me and I ask this multiple times because everyone has a different approach to this problem, which is now you have a layer of managers who are actually dealing with developers, right? And there are situations you would want to really understand what's exactly going on, how things are quality-wise, speed-wise, and you really don't have that much time that you go out and talk to 45 engineering leaders , engineering managers, engineers, to understand what's exactly going on with them. So, there must be some approach that you are following to have that visibility because you can't just go blind and just say, "Okay, whatever engineering managers are doing, how I'm coaching them would work out wonders." You have to like trust them, but then you have to have a check. You have to understand what exactly is going on. So how do you manage that piece as a director here at Betterworks?
C S Sriram: Yeah, no, that was a very interesting coaching experience for me, where I worked with each of my managers for almost over six months to help them build that discipline. Like any good software engineer will tell you, pulling is never a good idea. If you think of your manager as a software service, you don't want to ask them every half an hour or one hour 'what's the update?' Uh, I like push-based updates. So I help them set up dashboards. So you know, dashboards that talk to them about their team's delivery, their team's quality, uh, their team's motivation and general status and all of it. Uh, and I work with them to design it for their purpose. Uh, I think that was the first thing that I was very clear about. This is not a dashboard that I'm designing so that they can present a story to me, but it's a dashboard that they are using to solve their problems and I'm just peeking in to see what's happening. So that made it very usable. I use those dashboards to inform myself. I ask the questions that I would expect a manager to ask from them. And over time, you know, they got into the habit of asking it themselves because in every 1-on-1 we'd spend 10-15 minutes discussing those numbers. By the time we did it for three to six months, it had become internalized. They knew to look for, you know, signs, they knew to look for challenges. So that became quite natural from there on. And I again want to emphasize on that one part that these were dashboards that were designed to solve their problems. If there was a dashboard or information that I had to design to relay some information or story to a leadership team or to some other team or something like that, that would be something very different. But this is primarily a dashboard that a team uses to run itself. And I was just peeking into that. I was just looking at it to gather some information for myself. So that made a big difference. The second thing that I also did was skip-level 1-on-1s. It took me, I think, almost six months to learn how to do skip-level 1-on-1s, uh, because the two challenges that I faced with skip-level 1-on-1s was it turned out to be another project status update session initially. I was getting the same information from 2-3 places, which was inefficient. It was also a waste of time for the engineers to come and report what they've already done. And the second thing also was, there were a lot of complaints coming in my skip-level 1-on-1s initially as well. And especially more so because many of the engineers that I was doing skip-level 1-on-1s with were engineers who I managed earlier. So I had to slowly cut that relationship and just connect them to their new managers. And I started turning the skip-level 1-on-1s into sessions where I can coach and I can give people some guidance. And I can also use it to get the pulse of the team. Like, is the team generally positive or is the team generally frustrated? And who are the second-level leaders that I need to be aware of? Whose stories I have to carry on? Who I think can become the core of the business after my first-level leaders? So I changed the purpose of the skip-level 1-on-1s and over time that also developed into a good thing.
Kovid Batra: Great. Great. there is a lot that we can go in and talk about this, but we are running out of time. So I will put a pause to this session here, but before we end this session for us, I would love for you to share one of those best learnings that you think as an engineering leader made you an accomplished one, and you think that can drive real growth for someone who is in that position and looking for the next job.
C S Sriram: Got it. Yeah. The one thing that, uh, was a breakthrough learning for me was mentorship and coaching. My then boss, uh, who moved on to another company, I spoke with him and I turned him into a mentor. His name is Chris Lanier. Uh, he's an exceptional executive. I connect with him very regularly to discuss a lot of challenges that I face. It helps me in two ways. The first thing it helps me is I get an outsider's perspective to solve certain problems that, uh, I can't even take to my leaders because those are problems that I am expecting no answers for. So that is the first thing that I get. And the second thing is the more you grow in this career, the bigger the imposter syndrome gets. So that reassurance that someone with the kind of experience and the success that he has, still goes through all of those things; that's quite reassuring. You know, you steady yourself and then you move forward. The next thing that I would also recommend for anybody who is looking at going into this role is to get a coach. A coach is different from a mentor. A coach is going to diagnose challenges that you have and work on specific areas. Like I had two specific challenges, uh, about two years ago. Betterworks was really generous enough to give me a coach at that time. Challenge number one was that my peer-to-peer relationships were terrible. Like, I didn't have a relationship at all. It's not even that, you know, they were poor relationships. There's no relationships at all. Uh, an introvert like me, I didn't see the value of doing it as well. The second thing was public speaking skills. Almost 40% of my speaking was filler words. So I worked on both of those with the help of a coach and got those two addressed and they made a huge difference. So I would highly, and at this level, you can't afford unknown unknowns, like you can afford it at an engineer level. You can afford it at a manager level. If you don't know what you're missing, that can turn into a disaster for both the business and for you at the executive level. So a mentor and a coach are two things that I would highly recommend.
Kovid Batra: Makes sense. And I think I can't agree more on that front because we as humans have this tendency to be in our zones and think that, okay, whatever we are doing is fine and we are doing the right things. But when a third person perspective comes in, it humbles you down, gives you more perspective to look at things and improve way faster than you could have done from your own journey on or your own mistakes that you make. So I totally agree on that. And with that, I think, thanks a lot, Sriram. This was a really good experience.
C S Sriram: Yeah, sorry to, sorry to interrupt you. If you've got a minute, I did pick something to read. You asked at the beginning, something from my writing, do we have a minute for that?
Kovid Batra: Yes, for sure. Please go ahead.
C S Sriram: Cool. Perfect. Okay. This is something that I wrote in 2020. Uh, it's a poem called "No Magic". This is how it goes:
There is no magic in this world.
No magical letter shall arrive
to grant us freedom from the cupboard under the stairs,
and the tyrants who put us there.
No wizard shall scratch our door
with his mischievous staff
and pack us off unwilling on an adventure
that will draw forth our hidden courage.
No peddler shall sell us a flying horse
made of the darkest ebony
to exile us away to mystic lands
and there to find love and friendship.
No letters, no wizards, no winged horses.
In our lives of facts, laws, and immovable rules,
where trees don’t walk, beasts don’t talk,
and we don’t fly.
Except…
when we close our eyes and dream some dreams,
of magic missiles that bring us freedom,
of wily wizards that thrust us into danger,
of soaring speeds that lead us to destiny.
And thence we fly from life to hope and back again.
Birds that fly from the nest to sky and back again.
There is no magic in the world
but in the void of the nests of our mind.
The bird with its hollow bones,
where will it fly, if not in the unreachable sky?
Kovid Batra: Amazing! I mean, I could get like 60% of it, but I could feel what you are trying to say here. And I think it's within us that makes us go far, makes us go everywhere. It's not the magic, but we need to believe the magic that we have in us. So I think, a really inspiring one.
C S Sriram: Thanks. Thank you so much.
Kovid Batra: Great, Sriram, this session was really amazing. We would love to connect with you once again. Talk more about your current role, more into leadership. But for today, I think this is our time. Thank you so much. Thank you for joining.
C S Sriram: Absolutely. Thanks for having me, Kovid. I really enjoyed it.
In this episode of the groCTO by typo Podcast, host Kovid Batra speaks with Sheeya Gem, Director of Engineering and Product Strategy at ShareFile, about her experiences leading dev teams through mergers and acquisitions.
Sheeya discusses the importance of building collaborative relationships with stakeholders, maintaining effective communication, and fostering a shared purpose among teams. She emphasizes the significance of continuous learning, adaptability, and leveraging tools and processes to keep projects on track. The conversation also touches on managing cultural transitions, supporting teams through change, and ensuring successful integration post-acquisition. Finally, Sheeya shares valuable parting advice for engineering leaders, promoting trust, shared purpose & continuous learning.
Kovid Batra: Hi everyone. This is Kovid, back with another episode of the groCTO by typo podcast. Today with us, we have a special guest who has 20+ years of engineering and leadership experience. She’s not just a tech leader, but also an innovator, a business-minded person, which is a rare combination to find. Welcome to the show, Sheeya.
Sheeya Gem: Hi, Kovid. Thank you for inviting me. It’s a pleasure to join you today.
Kovid Batra: The pleasure is all ours. So Sheeya, guys, uh, let me introduce her a little bit more. Uh, she’s the Director of Engineering and Product Strategy at ShareFile. So ShareFile is a startup that was acquired by Progress from Citrix and, uh, the journey, uh, I was talking to Sheeya, was really interesting and that’s when we thought that we should conduct a podcast and talk about this, uh, merger and acquisition journey that she has gone through and talking about her leadership experiences. So today, uh, the, the main section would be around leading dev teams through mergers and acquisitions, and, uh, Sheeya would be taking us through that. But before we jump onto that section, uh, Sheeya, I think it’s a ritual. This is a surprise for you. Uh, so we get to know our guests a little more, uh, by knowing something which is deep down in their memory lane from their childhood or from their teenage, uh, that defines them today. So give us an introduction of yourself and some of those experiences from your childhood or teenage that define who you are today.
Sheeya Gem: Oh, you got me here. Uh, um, so my name is Sheeya Gem and, um, I am, I, I’m from Bangalore and, uh, grew up in Bangalore. This was when Bangalore was, was, was much smaller. Um, it was, uh, it was considered a retirees paradise back then. And, uh, growing up, my mom was a very strong, um, mentor and, and, and, and a figure in my life. She’d read to me when I was very young. Um, lots of stories, lots of novels, lots of books. So she was an English Lit major. And so, so she’d have all these plays. So I grew up listening to Shakespearean plays. Um, and, uh, one of the books that she’d read and it still sticks with me, and, and actually there’s, I actually have a little frame of this at this time. And it says, “She believed she could, so she did.” And it’s powerful. It’s powerful. Um, I’m sorry. I lost her a few years ago. And, uh, it’s, it’s defined me. It’s a big part of who I am, um, because at every stage in your life, and, and this has been true for me, um, at every stage I have challenged myself, and it’s, it’s my mom. It’s that voice. It says, “You can do what you need to do because you believe in it and you know it’s going to be true.”
Kovid Batra: I’m sorry for your loss, but I think she would be resting in peace and would be happy to see you where you are today and how she has inspired you to be who you are today. Uh.
Sheeya Gem: Thank you. Thank you.
Kovid Batra: All right, Sheeya. Thank you so much for sharing that and it means a lot. Uh, on that note, I think we can move on to the main section. Uh, yeah. Uh, so I think, uh, your journey at, at Progress ShareFile, uh, starts from the acquisition part, right? Uh, so tell us about how, how this acquisition happened and, uh, how things went at that time, some stories that would be, uh, lessons for the engineering leaders and engineering managers sitting out there listening to this.
Sheeya Gem: Yeah. Yeah. Um, so for most leaders who are part of an acquisition, you kind of are part of the conversations as you lead up to the, to the acquisition. And for ShareFile, this journey really started a few years ago. I’m just going to really quickly go through ShareFile’s story. ShareFile is a startup from Raleigh, North Carolina. Um, and it’s, it started up in the early 2000s and was bought by Citrix in 2012 and was part of the Citrix suite of products for, uh, for about 10 years, 10–12 years. And at that time, um, uh, a private equity group called Cloud Software Group acquired Citrix and as part of their portfolio, they have several other products as well. And that’s when ShareFile’s really acquisition journey started and as part of our strategy, ShareFile decided to go back to its roots and the roots of ShareFile was a vertical market strategy. And so for the past 2–3 years, um, and, and this was a fantastic ride because we got to innovate at a scale that we never could. CSG gave us the backing and the financing, the funding and the support and ShareFile had the right amount of talent to make things happen. As leadership, we knew that an acquisition was going to be our, our exit. So we were aware of that and we were very transparent with our, with our entire teams, everybody knew that an acquisition was on the radar. And as such, when Progress started talking to us, um, and ShareFile started sharing our financials, you know, how we do our business and all of those things, we, we knew it was, it was coming. So as such as leaders, you’re part of the journey that makes a successful exit. So the acquisition was a successful exit for us. And then it also starts the next part of your journey where you’re now with a company that has acquired you because they believe in your fundamentals, they believe in your team; and as leadership, it becomes important for us to make sure that that transition is successful and that merger goes as it needs to go.
Kovid Batra: So when you joined, uh, Progress, this was basically a new team coming into an existing company and that experience itself could be a little overwhelming. I haven’t gone through any such, uh, experience in my life, but I can just imagine and trying to relate here. That can be a little overwhelming because the culture completely changes. Um, you are in a setup where people know you, there is defined leadership which you are a part of, you’re part of the overall strategy and then defining, giving directions. But suddenly when you move here, things can change a lot culturally, as well as in terms of the goals and, uh, how things operate. So when this happened with you, was this an overwhelming experience or it came easily? And in either of the cases, how you handled it?
Sheeya Gem: Uh, was it an overwhelming experience? Um, not necessarily. It is an experience. It is different. And, and most humans coping with change and dealing with change is, is hard. And, um, and I think it’s important to recognize that different people are going to handle that change differently. And in many ways, it actually is almost the grieving of the loss of one thing before moving to the next thing, and as leaders, it’s important to make room for that, to give people a chance to, to absorb the change that’s happening, but to continue to be there to support, to provide that clarity, be transparent in what’s happening, where we’re going, and, and just knowing that, you know, some people are probably going to bounce right back. The two days they’re back, they’re okay. And some people are going, it’s going to take longer. It’s, it’s almost like those seven stages of grieving, uh, you know, and to make room for that and to know that, that kind of change from what was, people were comfortable with that, people probably excelled in that, going through the uncertainty of what is to come is a normal human reaction, and I think that’s where leaders shine, to know that this is a normal human reaction. I recognize it. I respect it. And I’m here for you when, when you’re ready to move to the next step.
Kovid Batra: Makes sense. So when you moved here, what exactly was your first initiative or what was that major initiative that you took up after moving in here that made you, uh, put down your feet and get back to work and outshine that, uh, outshine that particular initiative?
Sheeya Gem: Um, are you talking about post-acquisition, the steps that we took? Is that what you’re thinking about? Okay. So, all right. So maybe I could frame it this way. A company exists pre-acquisition. It has a set of goals. There’s a vision. There’s a strategy, right? Everybody is comfortable with it. You’re probably talking about it in your all-hands, in your small group meetings and every leadership meeting that you have in any kind of ‘ask me anything’. The leadership team is talking about what you’re saying. This is our vision. This is our goal. This is the strategy. Once the acquisition happens, you’re now looking at the goal, strategy, and vision of the new company. Now, likely they’re related because there was a reason that the acquiring company went ahead and bought this company. There’s a relationship there, but there’s also likely things that are going to be different. As an example, it’s possible, in our case, this is the situation, Progress has a heavy enterprise footprint. And so some of the strategy and goals are going to be a little different compared to, um, an SMB market where ShareFile continued to, uh, to excel. So, but are there commonalities? Yes. And, and I think this is where, again, leadership comes in where we say, “Hey, this is what we were pursuing. This continues to be our plan and our strategy. This is where ShareFile, Progress’ strategy comes in and in order to manage the transition and have success on both sides, we talk about what needs to happen next. And often what happens is in a mature acquisition, and this is often the case, there is a, there is, there’s plenty of time for companies to say, “Okay, I’m slowly going to bring in the new set of goals that we need to work towards.” Some companies don’t change at all. As an example, when IBM acquired Red Hat, for five years, Red Hat did what they always did. There was no change. Eventually, right, the goal started shifting and changing to align more with IBMs. So different companies have different trajectories. However, what’s common, what needs to happen is communication. Leaders need to be talking to their teams all the time, because without the communication, this is where that uncertainty creeps in. People don’t have the answers, so they start looking for answers and those answers may not be right. So at this time for leadership, it’s important to double down and say, “This is our strategy. This is a strategy for Progress. This is a transition plan to move towards a new strategy. Or it could be that for the next six months, guys, it is business as usual. We’re going to continue with our existing strategy. And over time, we’ll start bringing in aspects of the, of the acquiring company strategy.” So key thing here, support your teams, keep communicating.
Kovid Batra: So at that, during that phase, uh, what was your routine like? Every, uh, board meeting you had, after that, or every leadership meeting you had, you used to gather your team, communicate the things that you had with them, or you waited for a little while, uh, thought through things, how it should be put to your team? Because it’s, it’s a question of, uh, how you communicate it to your teams, because you understand them better, in what state they are, how they’re going to perceive it. So I’m just looking for some actionable here.
Sheeya Gem: Yeah.
Kovid Batra: Like how exactly you did that communication because having that communication definitely seems to be the key here. But how exactly it needs to be done? I want to know that.
Sheeya Gem: Yeah, yeah, you actually almost answered the question here. Uh, so you’re 100% right, right? You don’t necessarily come out and throw little bits of information here and there because that’s not a coherent strategy. Yes, the leadership is continuing to meet and it’s okay to tell your teams that the leadership, leadership teams are continuing to meet and are working through this. But yes, eventually, when we are in a place where we have a handle on how we’re going to do things, that’s when the communication comes up. Like I said, it’s important for teams to know, yes, we’re working with you, we’re thinking through things and then set a clear date, call the meetings, it’s usually like an all-hands kind of situation and then plenty of time for Q&A, gather your teams and present in a format that’s, that’s most comfortable for that culture. And, and sometimes it’s, it’s an ‘ask me anything’ kind of format. Sometimes it’s a chat by the fire kind of, kind of informal thing. And sometimes, and we actually did this year. We did an all-hands, had plenty of time for Q&A, and that evening we took our teams to the closest hangout place that we have. We usually gather there Thursday evenings for beer, and leadership was there and we answered questions. It was an informal setting and sometimes it’s important to, to, you know, go to a location that’s not your usual place of work. So a good restaurant, um, a place where you can maybe just, just chill a little bit, right? And, and, and have those conversations and there you’re able to meet people where they are and then connect with them on that 1-on-1 level and, and maybe answer questions a little bit more deeply.
Kovid Batra: One thing if I have to ask you, which you think you could have done better during that phase, uh, would be?
Sheeya Gem: What could I have done better? Um, it’d be terrible to say we got everything right. Uh, so here’s the thing. No matter how well you manage this, because remember I said that everybody’s going to go through those different stages of change, you will always see people where somebody is, is more agitated, feeling a little bit more anxious than other, right? And, and by, just by the reality of communications, where we say, “Okay, a month from now, we’re going to address this.” There are some people who are going to hit that stage of ‘I need to know now’ two weeks before that. And in that situation, it’s hard, but maybe what people can do is if you’re close enough to that, to be able to just reassure people a little bit more. Um, I think that’s something that, that I certainly could have done a little bit more of, but it’s also one of the situations where you’re kind of like weighing it. How much do I, should I be talking about this where not everything is clear and how much should I just hold? Um, so, so there is that balanced conversation that happens.
Kovid Batra: And in that situation, do you think is it okay to come out and say that I am in a phase where even I am processing the information? More like being vulnerable, I would say. Not exactly vulnerable, but saying that we are in a phase where we are processing things. I don’t want to say anything which, uh, maybe makes you more anxious instead of giving you more certainty at this phase. So making statements like this as a leader, is it okay?
Sheeya Gem: I think it is. I think it’s important to your point. Vulnerability is key where you trust your teams and you’re expecting them to trust you. So showing that vulnerable side, uh, builds empathy and helps people, uh, relate to you more. Um, what I would be careful though is some people could perceive that differently. Oh, leadership doesn’t have all the answers. So yeah, know your audience, know your audience.
Kovid Batra: Makes sense. Yeah, all right. I think, uh, this was really interesting. Anything, uh, Sheeya, uh, that you think had really driven you and made you who you are as an engineering leader in your whole career, not just at ShareFile, but in general I’m asking, what are those few principles or frameworks that have really worked out for you as a good leader?
Sheeya Gem: Yeah, um, I think it’s learning. For me, I, I have this desire to learn and, um, and I believe that no matter a situation, right, you can have a good situation or you could have a bad situation. No matter the situation, though, where you win is learning, learning from the situation, no matter what that situation is. So when you exit that situation, you have learned, you are a better person because you have learned from that situation. So, so that’s, that’s a big takeaway for me and, and something that, that I, maybe your audience will enjoy and that is for humans, you know, there are some things that are going to go really, really well and some things that are going to be downright awful and I think that’s life. But in each of these situations of the mindset is, “Hey, I’m put in a situation that I haven’t dealt with before. What can I take away from this?” You exit that situation as a winner, no matter what the situation was. And I’ve applied that through my life where, um, I’ve, I’ve, I’ve had the, uh, the good luck to work at some fantastic companies and, and be mentored by, by amazing people, um, from Etrade to eBay, uh, Citrix, several companies along the way. And at each of them, uh, when I changed jobs, I went into a job that was just a little different from what I did, and it kind of like opened up things for me. Um, and it helps you learn. So that would be a good takeaway where every time you go into something, try something just a little different. Uh, it changes your perspective. It, it builds empathy. When you do a little bit of marketing, you now have empathy for your marketing department a little bit more. When you do a little bit of work that, that’s not just pure engineering, it helps you see things in a different light and gives you a different perspective.
Kovid Batra: Touching on the marketing bit. I think, uh, the last time when we were talking, you mentioned that you have this urge, you have this curiosity all the time, and I think it’s driven from the same fact, learning, that you work with different teams to understand them more. So do you have any experience, like very specific experience where you had a chat with a sales guy or a marketing team person that led you to build something, like engineer something and build something for the customers?
Sheeya Gem: Yeah, yeah. Uh, that’s a good topic. Um, a part of leadership is besides guiding your teams, it’s about the collaborative relationships you build with other stakeholders. And a lot of people, when we hear the word ‘stakeholder’, we kind of like mentally take a step back. But what if we consider all of those stakeholders, people who are in that journey together with us? Because ultimately, that’s why they’re here. Um, it’s to be successful. And to define success in a way that resonates with each person is the concept of building collaborative relationships. It goes to the heart of shared purpose. Um, so as we were building some new innovative products, um, and, and I, ShareFile is a tech company and which means the product is tech. Who knows more about the product and the tech than the engineers who are building it, right? They are the builders However, all of the other stakeholders that we’re talking about are instrumental to making the product successful. That’s why they’re all here. So for me, it started becoming a case of saying that “Hey, we have uncovered this new way to do something and we believe there is an audience for this. There is a market for this.” Then the first set of people that we start talking to is being able to work with product management to say, “ What do you see? What have you seen in the field? You’re talking to customers all the time.” And it becomes, starts becoming this, this little bit of a cycle where they feed information to you and you’re feeding information back and it’s a loop. It’s, it’s becoming this loop that’s continuing to build and continuing to grow. Um, so there is a, there’s a fantastic book. Um, I think it’s called ‘Good to Great’. Um, and in that the author talks about the flywheel effect and that’s exactly what this is. So as you’re talking to product and you’re building that, that, that coherent thought of, “Okay, I have something here. I may have something really, really big.” The next step is talking to sales because sales tends to be the biggest cheerleader of the product in the market. They’re selling. This is their whole goal. They are your cheerleaders. And so then the next step of building that relationship with sales and saying, “Hey guys, what are you seeing? If I were to build something like this, what do you see, um, in the way it plays out in the market?” And you put that early version of the product in front of sales. Give them a prototype. Ask them to play with it. And most companies don’t tend to do this because sometimes there are walls, sometimes there’s a little bit of a, does sales really want to look at my prototype? They do, because that’s how they know what’s coming next. You’re opening that channel up, right? Similarly with marketing, to be able to say, I have something here. Do you think we could do some marketing spend to move this forward? And just like that you’ve built shared purpose because you’ve defined what success looks like for each group.
Kovid Batra: Right. That’s really interesting. And the, the last word ‘shared purpose’, I think that brings in more, uh, enthusiasm and excitement in individuals to actually contribute towards the same thing as you’re doing. And on that note, I, I think, uh, I would love to know something from you about how you have been bringing this shared purpose, particularly in the engineering team. So just now you mentioned that there could be, uh, walls which would prevent you from bringing out that prototype to the sales team, right? So in that exact situation, uh, what, what way do you think would work for teams, uh, and the leaders who are leading, let’s say, a group of, let’s say, 20 folks? I’m sure you’re leading a bigger team, but I’m just taking an example here that how do you take out that time, take out that bandwidth, uh, with the engineering team to work on the prototype? Because I’m sure the teams are always overloaded, right? They would always have the next feature to roll out. They would always have the next tech debt to solve, right? So how do you make sure that this feeling of shared purpose comes in and then people execute regardless of those barriers or how to overcome those barriers?
Sheeya Gem: Yes. Um, to have something like shared purpose work, you absolutely need the backing of your entire leadership org. And I’ve been very, very lucky to have that. Uh, from the Chief Product Officer to the CEO, to the Chief Technology Officer, we were aligned on this, completely and totally aligned on this. And so what this translate then, translates to then is investments, right? You talked about tech debt and how teams are always loaded, but if your entire leadership team is bought into that vision, then the way you set the investment profile itself is different, where you might say that, you know, half of the org is going to totally and completely focus on innovation. We are going to build this. Right. Then you have that, that organizational support. Now as leadership, as we are building that, when you start talking to your teams about the level of organizational support that you have, and remember, engineers want to build things that are successful with customers. Nobody wants to build something and put it on a shelf in their house. They want it on the market. That is the excitement of engineering. So to then be able to say that, “Hey! We believe in this. Our leadership believes in this. Our stakeholders are excited about this.” It’s the kind of excitement and adrenaline adrenaline pump that happens that nothing else gives that cheer. And that’s what we saw happen with our teams, that getting behind a vision, making that strategy your own, knowing that you are a key contributor to that success of the product and hence the success of the org, that is a vision that sustains and feeds itself. And, and that’s what we were able to build. Um, that’s something that I made the time for every day. You talk to your teams, you connect with your teams, you’re talking to your engineering managers, you’re talking to the principal engineers, and every time there is, there is concern, and there will be many, many concerns along the way, and I’m not going to have all the answers. That’s normal. I should not have all the answers, because if I have all the answers, then the thinking is limited to the max of my thinking, and a group’s thinking is always greater, right? The sum of that group’s thinking is always greater than any one individual’s thinking. So then it starts becoming a case of, this is the problem that we’re trying to solve. How best would we solve it? And when you put it in front of the brightest people in the room, the answers that you get to that problem, the solutions that you get, breaks through every bound that you can see.
Kovid Batra: So do you usually practice this? Like, uh, every week you have a meeting with your team and there are some folks who are actually working on the innovation piece or maybe not every week, maybe in a month? I, I am not sure about the cadets, but in general, what’s the practice like? How, how do you exactly make sure that that is happening and people are on track?
Sheeya Gem: Yeah, we actually meet every week and then any number of informal conversations throughout the day, right? You run into someone in the elevator, you have two minute conversation. You run into someone in the hallway, you have a two minute conversation. But yes, as leadership, we meet, uh, every week. And when I say leadership, and this is where my definition of leadership may be different from maybe some parts, some others. And, and, and to me, leadership is not just a title that’s given to someone. A lot of people think that one year, once you’re a manager, you’re a leader. The truth of it is, you’re going to see leaders in engineers, people who think differently, people who, um, who can drive something to success, people who can stand behind something because they know that area and know what to do next. They’re all leaders. So in my leadership meeting, I actually have a mix of engineering managers. I have principal engineers. I even have some, a couple of junior team leads because they are that good. And that group meets every week. And we talk about the biggest problems that we have and it becomes a group problem solving effort. We draw action items from that and then smaller groups form from there, solve, come back to the meeting next week and they talk about how they are, how they are going about it. So it is very much a team environment and a team success, um, metric the way we go behind things.
Kovid Batra: Makes sense. Um, one last thing that I would want to touch upon is that when you are doing all these communication, when you are making sure you’re learning, your team is having a shared purpose, everyone is driven towards the same goal, one thing that I feel is it is important to see how teams are moving, how teams are doing on different parameters, like how fast they’re moving, how good quality code is being produced there. And you mentioned, like you lead a team of almost a hundred people where there are few engineering managers and then engineers out there. As a Director of Engineering, there is no direct visibility into what exactly is happening on the ground. How do you ensure that piece, uh, in your position right now that everything which you think is important and critical, uh, is, is there, is on the tack on the track?
Sheeya Gem: Yeah, yeah, this is where tools come in. Also, very clear processes. Um, my recommendation is to keep the processes very lightweight because you don’t want people to be caught up in the administration of that process. But things like your hygiene, it’s important. You closed a story, close the story, right? Or let us know if you need help. Uh, so that becomes important. Um, there are lots of project management tools that are available on the market. Um, and again, like I said, lightweight, clear process. Uh, the ability to be able to, um, demonstrate work in progress, things like that. And that’s something else that we have. Um, we have this practice called show, tell and align and, um, we meet every week and this is all of engineering, and just like the title says, you show whatever you’ve got. And if you’re not in a position to show, you can talk about what you’ve got. And the purpose of it is to drive alignment and it’s, it’s, it’s an amazing meeting and we have a fantastic manager who runs that meeting. There’s a lot of energy there and we have no rules about what you can show or where you can show it. You know, some, some, some companies have rules like, oh, it needs to be in production for you to do. No, no, no, I want to see it if it’s on your dev laptop. I want to see it. Your team leads to want to see it. Uh, so we keep it very, very easy. And in that meeting, every senior leader who attends that meeting is encouraged to come in as an engineer and as an engineer only. Uh, they’re supposed to leave their titles at the door. It’s, it’s, it’s, it’s, it’s a challenge. It’s a challenge, but no one can come in and say, “Hey, I didn’t approve that!” Because you’re coming to this meeting as an engineer, which means if, if, and sometimes we’ve had, you know, directors and VPs who have something to share because they’re able to leave the title at the door. Uh, so it’s, it’s been a great practice for us, this ability to, to show our work in progress. Um, “Oh, look, I got this done.” Uh, “Here’s a little notification tab that I was able to build in three days. I’m going to show this to the team.” Or, or “Here’s a new framework that I’m thinking about and I found this. I’m going to show this to the team.” Uh, so this is a regular practice, um, at ShareFile and now at Progress.
Kovid Batra: Perfect. Perfect. Great, Sheeya. I think, uh, this was a really, really interesting talk, uh, learning about communication, learning about learning all the time, having a shared purpose. Show, tell, and align, that was interesting on the last piece. So I think with this, uh, we, we come to the end of this episode. It was really, really nice to have you here and we would love to have you again. Is there any parting advice for our audience that you would like to share? Uh, most of us are like engineering managers, aspiring engineering leaders or engineering leaders. If you would like to share, please go ahead.
Sheeya Gem: Um, we covered a lot of topics today, didn’t we? Um..
Kovid Batra: Yeah.
Sheeya Gem: Uh, what do I have for our, um, for our engineering managers? Trust your teams, but trust and verify. Um, and this is where, you know, some of the things we talked about, things like OKRs, things about lightweight process comes in. Trust, but verify. That’s important. Uh, the second part of it is shared purpose. You want to build that across your, not just your teams, but all of the stakeholders that you’re interacting with. So people are driving in the same direction, uh, and we’re all moving towards the same success and the same set of goals and every opportunity is a learning opportunity.
Kovid Batra: Great! Thank you, Sheeya. Thank you so much once again. Great to have you today.
Sheeya Gem: It was a pleasure. Thank you for inviting me on your show.
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In the second session of the 'Unlocking Engineering Productivity' webinar by Typo, host Kovid Batra engages engineering leaders Cesar Rodriguez and Ariel Pérez in a conversation about building high-performing development teams.
Cesar, VP of Engineering at StackGen, shares insights on ingraining curiosity and the significance of documentation and testing. Ariel, Head of Product and Technology at Tinybird, emphasizes the importance of clear communication, collaboration, and the role of AI in enhancing productivity. The panel discusses overcoming common productivity misconceptions, addressing burnout, and implementing effective metrics to drive team performance. Through practical examples and personal anecdotes, the session offers valuable strategies for fostering a productive engineering culture.
Kovid Batra: Hi everyone, welcome to the second webinar session of Unlocking Engineering Productivity by Typo. I’m your host, Kovid, excited to bring you all new webinar series, bringing passionate engineering leaders here to build impactful dev teams and unlocking success. For today’s panel, we have two special guests. Uh, one of them is our Typo champion customer. Uh, he’s VP of Engineering at StackGen. Welcome to the show, Cesar.
Cesar Rodriguez: Hey, Kovid. Thanks for having me.
Kovid Batra: And then we have Ariel, who is a longtime friend and the Head of Product and Technology at Tinybird. Welcome. Welcome to the show, Ariel.
Ariel Pérez: Hey, Kovid. Thank you for having me again. It’s great chatting with you one more time.
Kovid Batra: Same here. Pleasure. Alright, um, so, Cesar has been with us, uh, for almost more than a year now. And he’s a guy who’s passionate about spending quality time with kids, and he’s, uh, into cooking, barbecue, all that we know about him. But, uh, Cesar, there’s anything else that you would like to tell us about yourself so that, uh, the audience knows you a little more, something from your childhood, something from your teenage? This is kind of a ritual of our show.
Cesar Rodriguez: Yeah. So, uh, let me think about this. So one of, one of the things. So something from my childhood. So I had, um, I had the blessing of having my great grandmother alive when I was a kid. And, um, she always gave me all sorts of kinds of food to try. And something she always said to me is, “Hey, don’t say no to me when I’m offering you food.” And that stayed in my brain till.. Now that I’m a grown up, I’m always trying new things. If there’s an opportunity to try something new, I’m always, always want to try it out and see how it, how it is.
Kovid Batra: That’s, that’s really, really interesting. I think, Ariel, , uh, I’m sure you, you also have some something similar from your childhood or teenage which you would like to share that defines who you are today.
Ariel Pérez: Yeah, definitely. Um, you know, thankfully I was, um, I was all, you know, reminded me Cesar. I was also, uh, very lucky to have a great grandmother and a great grandfather, alive, alive and got to interact with them quite a bit. So, you know, I think we know very amazing experiences, remembering, speaking to them. Uh, so anyway, it was great that you mentioned that. Uh, but in terms of what I think about for me, the, the things that from my childhood that I think really, uh, impacted me and helped me think about the person I am today is, um, it was very important for my father who, uh, owned a small business in Washington Heights in New York City, uh, to very early on, um, give us the idea and then I know that in the sense that you’ve got to work, you’ve got to earn things, right? You’ve got to work for things and money just doesn’t suddenly appear. So at least, you know, a key thing there was that, you know, from the time I was 10 years old, I was working with my father on weekends. Um, and you know, obviously, you know, it’s been a few hours working and doing stuff and then like doing other things. But eventually, as I got older and older through my teenage years, I spent a lot more time working there and actually running my father’s business, which is great as a teenager. Um, so when you think about, you know, what that taught me for life. Obviously, there’s the power of like, look, you’ve got to work for things, like nothing’s given to you. But there’s also the value, you know, I learned very early on. Entrepreneurship, you know, how entrepreneurship is hard, why people go follow and go into entrepreneurship. It taught me skills around actual management, managing people, managing accounting, bookkeeping. But the most important thing that it taught me is dealing with people and working with people. It was a retail business, right? So I had to deal with customers day in and day out. So it was a very important piece of understanding customers needs, customers wants, customers problems, and how can I, in my position where I am in my business, serve them and help them and help them achieve their goals. So it was a very key thing, very important skill to learn all before I even went to college.
Kovid Batra: That’s really interesting. I think one, Cesar, uh, has learned some level of curiosity, has ingrained curiosity to try new things. And from your childhood, you got that feeling of building a business, serving customers; that is ingrained in you guys. So I think really, really interesting traits that you have got from your childhood. Uh, great, guys. Thank you so much for this quick sweet intro. Uh, so coming to today’s main section which is about talking, uh, about unlocking engineering productivity. And today’s, uh, specifically today’s theme is around building that data-driven mindset around unlocking this engineering productivity. So before we move on to, uh, and deep dive into experiences that you have had in your leadership journey. First of all, I would like to ask, uh, you guys, when we talk about engineering productivity or developer productivity, what exactly comes to your mind? Like, like, let’s start with a very basic, the fundamental thing. I think Ariel, would you like to take it first?
Ariel Pérez: Absolutely. Um, the first thing that comes to mind is unfortunate. It’s the negative connotation around developer productivity. And that’s primarily because for so long organizations have trying to figure out how do I measure the productivity of these software developers, software engineers, who are one of my most expensive resources, and I hate the word ‘resource’, we’re talking about people, because I need to justify my spend on them. And you know what, they, I don’t know what they do. I don’t understand what they do. And I got to figure out a way to measure them cause I measure everyone else. If you think about the history of doing this, like for a while, we were trying to measure lines of code, right? We know we don’t do that. We’re trying to open, you know, we’re trying to, you know, measure commits. No, we know we don’t do that either. So I think for me, unfortunately, in many ways, the term ‘developer productivity’ brings so many negative associations because of how wrong we’ve gotten it for so long. However, you know, I am not the, I am always the eternal optimist. And I also understand why businesses have been trying to measure this, right? All these things are inputs into the business and you build a business to, you know, deliver value and you want to understand how to optimize those inputs and you know, people and a particular skill set of people, you want to figure out how to best understand, retain the best people, manage the best people and get the most value out of those people. The thing is, we’ve gotten it wrong so many times trying to figure it out, I think, and you know, some of my peers who discuss with me regularly might, you know, bash me for this. I think DORA was one good step in that direction, even though there’s many things that it’s missing. I think it leans very heavily on efficiency, but I’ll stop, you know, I’ll leave that as is. But I believe in the people that are behind it and the people, the research and how they backed it. I think a next iteration SPACE and trying to go to SPACE, moved this closer and tried to figure it out, you know, there’s a lot of qualitative aspects that we need to care about and think about. Um, then McKinsey came and destroyed everything, uh, unfortunately with their one metric to rule it all. And it was, it’s been all hell broke loose. Um, but there’s a realization and a piece that look, we, as, as a, as a, as an industry, as a role, as a type of work that we do, we need to figure out how we define this so that we can, you know, not necessarily justify our existence, but think about, how do we add value to each business? How do we define and figure out a better way to continually measure? How do we add value to a business? So we can optimize for that and continually show that, hey, you actually can’t live without us and we’re actually the most important part of your business. Not to demean any other roles, right? But as software engineers in a world where software is eating the world and it has eaten the world, we are the most important people in the, in there. We’re gonna figure out how do we actually define that value that we deliver. So it’s a problem that we have to tackle. I don’t think we’re there yet. You know, at some point, I think, you know, in this conversation, we’ll talk about the latest, the latest iteration of this, which is the core 4, um, which is, you know, things being talked about now. I think there’s many positive aspects. I still think it’s missing pieces. I think we’re getting closer. But, uh, and it’s a problem we need to solve just not as a hammer or as, as a cudgel to push and drive individual developers to do more and, and do more activity. That’s the key piece that I think I will never accept as a, as a leader thinking about developer productivity.
Kovid Batra: Great, I think that that’s really a good overview of how things are when we talk about productivity. Cesar, do you have a take on that? Uh, what comes to your mind when we talk about engineering and developer productivity?
Cesar Rodriguez: I think, I think what Ariel mentioned resonates a lot with me because, um, I remember when we were first starting in the industry, everything was seen narrowly as how many lines of code can a developer write, how many tickets can they close. But true productivity is about enabling engineers to solve meaningful problems efficiently and ensuring that those problems have business impact. So, so from my perspective, and I like the way that you wrote the title for this talk, like developer (slash) engineering. So, so for me, developer, when I think about developer productivity, that that brings to my mind more like, how are your, what do your individual metrics look like? How efficiently can you write code? How can you resolve issues? How can you contribute to the product lifecycle? And then when you think about engineering metrics, that’s more of a broader view. It’s more about how is your team collaborating together? What are your processes for delivering? How is your system being resilient? Um, and how do you deliver, um, outcomes that are impactful to the business itself? So I think, I think I agree with Ariel. Everything has to be measured in what is the impact that you’re going to have for the business because if you can’t tie that together, then, then, well, I think what you’re measuring is, it’s completely wrong.
Kovid Batra: Yeah, totally. I, I, even I agree to that. And in fact, uh, when we, when we talk about engineering and developer productivity, both, I think engineering productivity encompasses everything. We never say it’s bad to look at individual productivity or developer productivity, but the way we need to look at it is as a wholesome thing and tie it with the impact, not just, uh, measuring specific lines of code or maybe metrics like that. Till that time, it definitely makes sense and it definitely helps measure the real impact, uh, real improvement areas, find out real improvement areas from those KPIs and those metrics that we are looking at. So I think, uh, very well said both of you. Uh, before I jump on to the next piece, uh, one thing that, uh, I’m sure about that you guys have worked with high-performing engineering teams, right? And Ariel, you had a view, like what people really think about it. And I really want to understand the best teams that you have worked with. What’s their perception of, uh, productivity and how they look at, uh, this data-driven approach, uh, while making decisions in the team, looking at productivity or prioritizing anything that comes their way, which, which would need improvement or how is it going? How, how exactly these, uh, high-performing teams operate, any, any experiences that you would like to share?
Ariel Pérez: Uh, Cesar, do you want to start?
Cesar Rodriguez: Sure. Um, so from my perspective, the first thing that I’ve observed on high-performing teams is that is there is great alignment with the individual goals to what the business is trying to achieve. Um, the interests align very well. So people are highly motivated. They’re having fun when they’re working and even on their outside hours, they’re just thinking about how are you going to solve the problem that they’re, they’re working on and, and having fun while doing it. So that’s, that’s one of the first things that I observed. The other thing is that, um, in terms of how do we use data to inform the decisions, um, high-performing teams, they always use, consistently use data to refine processes. Um, they identify blockers early and then they use that to prioritize effectively. So, so I think all ties back to the culture of the team itself. Um, so with high-performing teams, you have a culture that is open, that people are able to speak about issues, even from the lowest level engineer to the highest, most junior engineers, the most highest senior engineer, everyone is treated equally. And when people have that environment, still, where they can share their struggles, their issues and quickly collaborate to solve them, that, that for me is the biggest thing to be, to be high-performing as a team.
Kovid Batra: Makes sense.
Ariel Pérez: Awesome. Um, and, you know, to add to that, uh, you know, I 1000% agree with the things you just mentioned that, you know, a few things came to mind of that, like, you know, like the words that come to mind to describe some of the things that you just said. Uh, like one of them, for example, you know, you think about the, you know, what, what is a, what is special or what do you see in a high-performing team? One key piece is there’s a massive amount of intrinsic motivation going back to like Daniel Pink, right? Those teams feel autonomy. They get to drive decisions. They get to make decisions. They get to, in many ways own their destiny. Mastery is a critical thing. These folks are given the opportunity to improve their craft, become better and better engineers while they’re doing it. It’s not a fight between ‘should I fix this thing’ versus ‘should I build this feature’ since they have autonomy. And the, you know, guide their own and drive their own agenda and, and, and move themselves forward. They also know when to decide, I need to spend more time on building this skill together as a team or not, or we’re going to build this feature; they know how to find that balance between the two. They’re constantly becoming better craftsmen, better engineers, better developers across every dimension and better people who understand customer problems. That’s a critical piece. We often miss in an engineering team. So becoming better at how they are doing what they do. And purpose. They’re aligned with the mission of the company. They understand why we do what we do. They understand what problem we’re solving. They, they understand, um, what we sell, how we sell it, whose problems to solve, how we deliver value and they’re bought in. So all those key things you see in high-performing teams are the major things that make them high-performing.
The other thing sticking more to like data and hardcore data numbers. These are folks that generally are continually improving. They think about what’s not working, what’s working, what should we do more of, what should we do less of, you know, when I, I forgot who said this, but they know how to turn up the good. So whether you run retros, whether you just have a conversation every day, or you just chat about, hey, what was good today, what sucked; you know, they have continuous conversations about what’s working, what’s not working, and they continually refine and adjust. So that’s a key critical thing that I see in high-performing teams. And if I want to like, you know, um, uh, button it up and finish it at the end is high-performing teams collaborate. They don’t cooperate, they collaborate. And that’s a key thing we often miss, which is and the distinction between the two. They work together on their problems, which one of those key things that allows them to like each other, work well with each other, want to go and hang out and play games after work together because they depend on each other. These people are shoulder to shoulder every day, and they work on problems together. That helps them not only know that they can trust each other, they can trust each other, they can depend on each other, but they learn from each other day in and day out. And that’s part of what makes it a fun team to work on because they’re constantly challenging each other, pushing each other because of that collaboration. And to me, collaboration means, you know, two people, three people working on the same problem at the same time, synchronously. It’s not three people separating a problem and going off on their own and then coming back together. You know, basically team-based collaboration, working together in real time versus individual work and pulling it together; that’s another key aspect that I’ve often seen in high-performing teams. Not saying that the other ways, I have not seen them and cannot be in a high-performing team, but more likely and more often than not, I see this in high-performing teams.
Kovid Batra: Perfect. Perfect. Great, guys. And in your journeys, um, there have been, there must have been a lot of experiences, but any counterintuitive things that you have realized later on, maybe after making some mistakes or listening to other people doing something else, are there any things which, which are counterintuitive that you learned over the time about, um, improving your team’s productivity?
Ariel Pérez: Um, I’ll take this one first. Uh, I don’t know if this is counterintuitive, but it’s something you learn as you become a leader. You can’t tell people what to do, especially if they’re high-performing, you’re improving them, even if you know better, you can’t tell them what to do. So unfortunately, you cannot lead by edict. You can do that for a short period of time and get away with it for a short period of time. You know, there’s wartime versus peacetime. People talk about that. But in reality, in many ways, it needs to come from them. It needs to be intrinsic. They’re going to have to be the ones that want to improve in that world, you know, what do you do as a leader? And, you know, I’ve had every time I’ve told them, do this, go do this, and they hated me for it. Even if I was right at the end, then even if it took a while and then they eventually saw it, there was a lot of turmoil, a lot of fights, a lot of issues, and some attrition because of it. Um, even though eventually, like, yes, you were right, it was a bit more painful way, and it was, you know, me and the purpose for the desire, you know, let me go faster. We got to get this done. Um, it needs to come from the team. So I think I definitely learned that it might seem counterintuitive. You’re the boss. You get to tell people to do. It’s like, no, actually, no, that’s not how it works, right? You have to inspire them, guide them, drive them, give them the tools, give them the training, give them the education, give them the desire and need and want for how to get there, have them very involved in what should we do, how do we improve, and you can throw in things, but it needs to come from them. If there were anything else I’d throw into that, it was counterintuitive, as I think about improving engineering productivity was, to me, this idea of that off, you know, as we think about from an accounting perspective, there’s just no way in hell that two engineers working on one problem is better than one. There’s no way that’s more productive. You know, they’re going to get half the work done. That’s, that’s a counterintuitive notion. If you think about, if you think about it, engineers as just mere inputs and resources. But in reality, they’re people, and that software development is a team sport. As a matter of fact, if they work together in real time, two engineers at the same time, or god forbid, three, four, and five, if you’re ensemble programming, you actually find that you get more done. You get more done because things, like they need to get reworked less. Things are of higher quality. The team learns more, learns faster. So at the end of the day, while it might feel slow, slow is smooth and smooth is fast. And they get just get more over time. They get more throughput and more quality and get to deliver more things because they’re spending less time going back and fixing and reworking what they were doing. And the work always continues because no one person slows it down. So that’s the other counterintuitive thing I learned in terms of improving and increasing productivity. It’s like, you cannot look at just productivity, you need to look at productivity, efficiency, and effectiveness if you really want to move forward.
Kovid Batra: Makes sense. I think, uh, in the last few years, uh, being in this industry, I have also developed a liking towards pair programming, and that’s one of the things that align with, align with what you have just said. So I, I’m in for that. Yeah. Uh, great. Cesar, do you have, uh, any, any learnings which were counterintuitive or interesting that you would like to share?
Cesar Rodriguez: Oh, and this goes back to the developer versus engineering, uh, conversation, uh, and question. So productivity and then something that’s counterintuitive is that it doesn’t mean that you’re going to be busy. It doesn’t mean that you’re just going to write your code and finish tickets. It means that, and this is, if there are any developers here listening to this, they’re probably going to hate me. Um, you’re going to take your time to plan. You’re going to take your time to reflect and document and test. Um, and we, like, we’ve seen this even at StackGen last quarter, we focused our, our, our efforts on improving our automated tests. Um, in the beginning, we’re just trying to meet customer demands. We, unfortunately, they didn’t spend much time testing, but last quarter we made a concerted effort, hey, let’s test all of our happy paths, let’s have automated tests for all of that. Um, let’s make sure that we can build everything in our pipelines as best as possible. And our, um, deployment frequency metrics skyrocketed. Um, so those are some of the, uh, some of the counterintuitive things, um, maybe doing the boring stuff, it’s gonna be boring, but it’s gonna speed you up.
Ariel Pérez: Yeah, and I think, you know, if I can add one more thing on that, right, that’s critical that many people forget, you know, not only engineers, as we’re working on things and engineering leadership, but also your business peers; we forget that the cost of software, the initial piece of building it is just a tiny fraction of the cost. It’s that lifetime of iterating, maintaining it, managing, building upon it; that’s where all the cost is. So unfortunately, we often cut the things when we’re trying to cut corners that make that ongoing cost cheaper and you’re, you’re right, at, you know, investing in that testing upfront might seem painful, but it helps you maintain that actual, you know, uh, that reasonable burn for every new feature will cost a reasonable amount, cause if you don’t invest in that, every new feature is more expensive. So you’re actually a whole lot less productive over time if you don’t invest on these things at the beginning.
Cesar Rodriguez: And it, and it affects everything else. If you’re trying to onboard somebody new, it’ll take more time because you didn’t document, you didn’t test. Um, so your cost of onboarding new people is going to be more expensive. Your cost of adding new people, uh, new features is going to be more expensive. So yeah, a hundred percent.
Kovid Batra: Totally. I think, Cesar, documentation and testing, uh, people hate it, but that’s the truth for sure. Great, guys. I think, uh, there is more to learn on the journey and there are a lot more questions that I have and I’m sure audience would also have a lot of questions. So I would request the audience to put in their questions in the comment section right now, because at the end when we are having a Q&A, we’ll have all the questions sorted and we can take all of them one by one. Okay. Um, as I said, like a lot of learning and unlearning is going to happen, but let’s talk about some of, uh, your specific experiences, uh, learn some practical tips from there. So coming to you, Ariel. Uh, you have recently moved into this leadership role at Tinybird. Congratulations, first of all.
Ariel Pérez: Thank you.
Kovid Batra: And, uh, I’m sure this comes with a lot of responsibility when you enter into a new environment. It’s not just a new thing that you’re going to work upon, it’s a whole new set of people. I’m sure you have seen that in your career multiple times. But every time you step in and you’re a new person there, and of course, uh, you’re going as a leader, uh, it could be overwhelming, right? Uh, how do you manage that situation? How do you start off? How do you pull off so that you actually are able to lead, uh, and, and drive that impact which you really want?
Ariel Pérez: Got it. Um, so, uh, the first part is one of, this may sound like fluff, but it really helps, um, in many ways when you have a really big challenge ahead, you know, you have to avoid, you have to figure out how to avoid letting imposter syndrome freeze you. And even if you’ve had a career of success, you know, in many ways, imposter syndrome still creeps up, right? So how do I fight, how do I fight that? It’s one of those things like stand in front of the mirror and really deep breaths and talk about I got this job for a reason, right? I, you know, I, I, they, they’re trusting me for a reason. I got here. I earned this. Here’s my track record. I worked this. Like I deserve to be here. I’m supposed to be here. I think that’s a very critical piece for any new leader, especially if you’re a new leader in a new place, because you have so much novelty left and right. You have to prove yourself and that’s very daunting. So the first piece is you need to figure out how to get yourself out of your own head. And push yourself along and coach yourself, like I’m supposed to be here, right? Once you get that piece, you know down pat, it really helps in many ways helps change your own mindset your own framing. When you’re walking into conversations walking into rooms, there’s a big piece of how, how that confidence shines through. That confidence helps you speak and get your ideas and thoughts out without tripping all over yourself. That confidence helps you not worry about potentially ruffling some feathers and having hard conversations. When you’re in leadership, you have to have hard conversations. It’s really important to have that confidence, obviously without forgetting it, without saying, let me run over everybody, cause that’s not what it means, but it just means you got to get over the piece that freezes you and stops you. So that’s the first piece I think. The second piece is, especially when moving higher and higher into positions of leadership; it’s listening. Listening is the biggest thing you do. You might have a million ideas, hold them back, please hold them back. And that’s really hard for me. It’s so hard cause I’m like, “I see that I can fix that. I can fix that too. I’ve seen that before I can fix it too.” But, you know, you earn more respect by listening and observing. And actually you might learn a few things or two. I’m like, “Oh, that thing I wanted to fix, there’s a reason why it’s the way it is.” Because every place is different. Every place has a different history, a different context, a different culture, and all those things come into play as to why certain decisions were made that might seem contrary to what you would have done. And it helps you understand that context. That context is critical, not only to then figure out the appropriate solution to the problem, but also that time while you’re learning and listening and talking to people, you’re building relationships with people, you’re connecting to people, you’re understanding, you’re understanding the players, understanding who does well, who doesn’t do well, you’re understanding where all the bodies are buried, you’re understanding the strategy, you’re getting a big picture of all the things so that then when it comes time to say now time to implement change, you have a really good setup of who are the people that are gonna help me make the change, who are the people that are going to be challenging, how do I draw a plan to do change management, which is a big important thing. Change management is huge. It’s 90% people. So you need to understand the people and then understand, it also gives you enough time to understand the business strategy, the context, the big problem where you’re going to kind of be more effective at. Here’s why I got hired. Now I’m going to implement the things to help me execute on what I believe is the right strategy based on learning and listening and keeping my mouth shut for the time, right? Now, traditionally, you’ll hear this thing about 90 days. I think the 90 days is overly generous if you’re in a really big team, I think it leans and skews toward big places, slower moving places, um, and, and places that move. That’s it. Bigger places, slower places. When you join a startup environment, we join a smaller company. You need to be able to move faster. You don’t have 90 days to make decisions. You don’t have 90 days. You might have 30 days, right? You want to push that back as far as you can to get an appropriate context. But there’s a bias for action, reasonably so because you’re not guaranteed that the startup is going to be there tomorrow. So you don’t have 90 days, but you definitely don’t want to do it in two weeks and probably not start doing things in a month.
Kovid Batra: Makes sense. Makes sense. So, uh, a follow-up question on that. Uh, when you get into this position, if you are in a startup, let’s say you get 30 to 45 days, but then because of your bias towards action, you pick up initiatives that you would want to lead and create that impact. In your journey at Tinybird, have you picked up something, anything interesting, maybe related to AI or maybe working with different teams that you think would work on your existing code base to revamp it, anything that you have picked up and why?
Ariel Pérez: Yeah, a bunch of stuff. Um, I think when I first joined Tinybird, my first role was field CTO, which is a role that takes the, the, the responsibilities of the CTO and the external facing aspects of them. So I was focused primarily on the market, on customers, on prospects. And as part of that one, you know, one of the first initiatives I had was how do we, uh, operate within the, you know, sales engineering team, who was also reporting to me, and make that much more effective, much more efficient. So a few of the things that we were thinking of there were, um, AI-based solutions and GenAI-based solutions to help us find the information we need earlier, sooner, faster. So that was more like an optimization and efficiency thing in terms of helping us get the answers and clarify and understand and gather requirements from customers and very quickly figure out this is the right demo for you, these are the right features and capabilities for you, here’s what we can do, here’s what we can’t do, to get really effective and efficient at that. When moving into a product role though, and product and engineering role, in terms of the, the latest initiatives that I’ve picked up, like there, there, there, there are two big things in terms of themes. One of them is that Tinybird must always work, which sounds like, yeah, well, duh, obviously it must always work, but there’s a key piece underpinning that. Number one, obviously the, you know, stability and reliability are huge and required for trust from customers wanting to use you as a dev tool. You need to be able to depend on it, but there’s another piece is anything I must do and try to do on the platform, it must fail in a way that I understand and expect so that then I can self service it and fix it. So that idea of Tinybird always works that I’ve been picking up and working on projects is transparency, observability, and the ability for customers to self-service and resolve issues simply by saying, “I need more resources.” And that’s a, you know, it’s a very challenging thing because we’ve got to remove all the errors that have nothing to do with that, all the instability and all the reliability problems so that those are granted. And then remaining should only be issues that, hey, customer, you can solve this by managing your limits. Hey, customer, you can solve this by increasing the cores you’re using. You can solve this by adding more memory and that should be the only thing that remains. So working on a bunch of stuff there on predicting whether something will fail or not, predicting whether something is going to run out of resources or not, very quickly identifying if you’re running out of resources so there’s almost like an SRE monitoring observability aspect to this, but turning that back into a product solution. That’s one side of it. And then the other big pieces will be called a developer’s experience. And that’s something that my, you know, my, my, my peer is working internally on and leading is a lot more about how developers develop today. Developers develop today, well, they always develop locally. They prefer not depending on IO on a network, but developer, every developer, whether they tell you yes or no, is using an AI assistant; every developer, right? Or 99% of developers. So the idea is, how do we weave that into the experience without making it be, you know, a gimmick? How do we weave an AI Copilot into your development experience, your local development experience, your remote development experience, your UI development experience so that you have this expert at your disposal to help you accelerate your development, accelerate your ability to find problems before you ship? And even when you ship, help you find those problems there so you can accelerate those cycles, so you can shorten those lead time, so you can get to productivity and a productive solution faster with less errors and less issues. So that’s one major piece we’re working on there on the embedding AI; and not just AI and LLMs and GenAI, all you think about, even traditional. I say traditional like ML models on understanding and predicting whether something’s going to go wrong. So we’re working on a lot of that kind of stuff to really accelerate the developer, uh, accelerate developer productivity and engineering team productivity, get you to ship some value faster.
Kovid Batra: Makes sense. And I think, uh, when you’re doing this, is there any kind of framework, tooling or processes that you’re using to measure this impact, uh, over, over the journey?
Ariel Pérez: Yeah, um, for this kind of stuff, I lean a lot more toward the outcomes side of the equation, you know, this whole question of outputs versus outcomes. I do agree. John Cutler, very recently, I loved listening to John Cutler. He very recently published something like, look, we can’t just look at outcomes, because unfortunately, outcomes are lagging. We need some leading indicators and we need to look at not only outcomes, but also outputs. We need to look at what goes into here. We need to look at activity, but it can’t be the only thing we’ll look at. So the things I look at is number one, um, very recently I started working with my team to try to create our North Star metric. What is our North Star metric? How do we know that what we’re doing and what we’re solving for is delivering value for our customers? And is that linked to our strategy and our vision? And do we see a link to eventual revenue, right? So all those things, trying to figure out and come up with that, working with my teams, working, looking at our customers, understanding our data, we’ve come up with a North Star metric. We said, great, everything we do should move that number. If that moving, if that number is moving up into the right, we’re doing the right things. Now, looking at that alone is not enough, because especially as engineering teams, I got to work back and say, how efficient are we at trying to figure that out? So there’s, you know, a few of the things that I look at, I obviously look at the DORA metrics. I do look at them because they help us try to figure out sources of issues, right? What’s our lead time? What’s our cycle time? What’s our deployment frequency? What’s our, you know, what, you know, what, what’s our, uh, you know, change failure rate? What’s our mean time to recover? Those are very critical to understand. Are we running as a tip-top shop in terms of engineering? How good are we at shipping the next thing? Because it’s not just shipping things faster; it’s if there’s a problem, I need to fix it really fast. If I want to deliver value and learn, and this is the second piece is critical that many companies fail is, I need to put it out in the hands of customers sooner. That’s the efficiency piece. That’s the outputs. That’s the, you know, are we getting really good at putting it in front of customers, but the second piece that we must need independent of the North Star metric is ‘and what happened’, right? Did it actually improve things? Did it, did it make things worse. So it’s optimizing for that learning loop on what our customers are doing. Do we have.. I’m tracking behavioral analytics pieces where the friction points were funnels. Where are they dropping off? Where are they circling the wheels, right? We’re looking at heat maps. We’re looking at videos and screen shares of like, what did the customer do? Why aren’t they doing what they thought we thought they were going to do? So then now when we learn this, go back to that really awesome DORA numbers, ship again, and let’s see, let’s see, let’s fix on that. So, to me, it’s a comprehensive view on, are we getting really good at shipping? And are we getting really good at shipping the right thing? Mixing both those things driven by the North star metric. Overall, all the stuff we’re doing is the North star moving up into the right.
Kovid Batra: Makes sense. Great. Thanks, Ariel. Uh, this was really, really insightful. Like, from the point you enter as a leader, build that listening capability, have that confidence, uh, driving the initiatives which are right and impactful, and then looking at metrics to ensure that you’re moving in the right direction towards that North Star. I think to sum up, it was, it was really nice and interesting. Cesar, I think coming to your experience, uh, you have also had a good stint at, uh, at StackGen and, uh, you were mentioning about, uh, taking up this transition successfully, uh, which was multi-cloud infrastructure that expanded your engineering team. Uh, right? And I would want to like deep dive into that experience. Uh, you specifically mentioned that, uh, that, uh, transition was really successful, and at that time, you were able to keep the focus, keep the productivity in place. How things went for you, let’s deep dive into that experience of yours.
Cesar Rodriguez: Yeah. So, so from my perspective, the goals that you are going to have for your team are going to be specific to where the business is at, at that point in time. So, for example, StackGen, we started in 2023. Initially, we were a very small number of engineers just trying to solve the initial problem, um, which we’re trying to solve with Stackdn, which is infrastructure from code and easily deploying cloud architecture into, into the cloud environment. Um, so we focus on one cloud provider, one specific problem, with a handful of engineers. And once we started learning from customers, what was working, what was not working, um, and we started being pulled into different directions, we quickly learned that we needed to increase engineering capacity to support additional clouds, to deliver additional features faster. Um, our clients were trying to pull us in different directions. So that required, uh, two things. One is, um, hiring and scaling the team quickly. So now we are, at the moment we’re 22 engineers; so hiring and scaling the engineering team quickly and then enabling new team members to be as productive as possible in day zero. Um, and this is where, this is where the boring, the boring actions come into play. Um, uh, so first of all, making sure that you have enough documentation so somebody can get up and running on day one, um, and they can start doing pull requests on day one. Second of all, making sure that you have, um, clear expectations in terms of quality and what is your happy path, and how can you achieve that. And third, um, is making sure everyone knows what is expected from them in terms of the metrics that we’re looking for and, uh, the quality that we’re looking for in their outcomes. And this is something that we use Typo for. So, for example, we have an international team. We have people in India, Portugal, US East Coast, US West Coast. And one of the things that we were getting stuck early on was our pull requests were getting opened, but then it took a really long time for people to review them, merge them, and take action and get them deployed. So, um, we established a metric, and we did this using Typo, where we were measuring, hey, if you have a pull request open more than 12 hours, let’s create an alert, let’s alert somebody, so that somebody can be on top of that. We don’t want to get somebody stuck for more than a working day, waiting for somebody to review the pull request. And, and the other metric that we look at, which is deployment frequency, we see that an uptick of that. Now that people are not getting stuck, we’re able to have more frictionally, frictionless, um, deployments to our SDLC where people are getting less stuck. We’re seeing collaboration between the team members regardless of their time zone improving. So that’s something actionable that we’ve implemented.
Kovid Batra: So I think you’re doing the boring things well and keeping a good visibility on things, how they’re proceeding, really helped you drive this transition smoothly, and you were able to maintain that productivity in the team. That’s really interesting. But touching on the metrics part again, uh, you mentioned that you were using Typo. Uh, there, there are, uh, various toolings to help you, uh, plan, execute, automate, and reflect things when you are, when you are into a position where as a leader, uh, you have multiple stakeholders to manage. So my question to both of you, actually, uh, when we talk about such toolings, uh, that are there in the market, like Typo, how these tools help you exactly, uh, in each of these phases, or if you’re not using such tools, you must be using some level of metrics, uh, to actually, let’s say you’re planning an initiative, how do you look at numbers? If you’re automating something and executing something, how do you look at numbers and how does this whole tooling piece help you in that? Um, yeah.
Cesar Rodriguez: I think, I think for me, the biggest thing before, uh, using a tool like Typo was it was very hard to have a meaningful conversation on how the engineering team was performing, um, without having hard, hard data and raw data to back it up. So, um, the conversation, if you don’t, if you’re not measuring things, it’s more about feelings and more about anecdotal evidence. But when you have actual data that you can observe, then you can make improvements, and you can measure how, how, how that, how things are going well or going bad and take action on it. So, so that’s the biggest, uh, for me, that’s the biggest benefit for, from my perspective. Um, you have, you can have conversations within your team and then with the rest of the organization, um, and present that in a, in a way that makes sense for everyone.
Kovid Batra: Makes sense. I think that’s the execution part where you really take the advantage of the tool. You mentioned with one example that you had set a goal for your team that okay, if the review time is more than 12 hours, you would raise an alert. So, totally makes sense, that helps you in the execution, making it more smooth, giving you more, uh, action-driven, uh, insights so that you can actually make teams move faster. Uh, Ariel, for you, any, any experiences around that? How do you, uh, use metrics for planning, executing, reflecting?
Ariel Pérez: So I think, you know, one of the things I like doing is I like working from the outside in. By that I mean, first, let me look at the things that directly impact customers, that is visible. There’s so much there on, you know, in terms of trust to customers. There’s also someone’s there on like actual eventual impact. So I lay looking, for example, um, the, it may sound negative, but it’s one of those things you want to track very closely and manage and then learn from is, what’s our incident number? Like, how many incidents do we have? You know, how many P0s? How many P1s? That is a very important metric to trust because I will guarantee you this, if you don’t have that number as an engineering leader, your CEO is going to try to figure out, hey, why are we having so many problems? Why are so many customers angry calling me? So that’s a number you’re going to want to have a very strong pulse on: understand incidents. And then obviously, take that number and try to figure out what’s going on, right? There’s so much behind it. But the first part is understand the number and you want that number to go down over time. Um, obviously, like I said, there’s a North star metric. You’re tracking that. Um, I look at also, which, you know, I don’t lean heavily on these, but they’re still used a lot and they’re still valuable. Things like NPS and CSAT to help you understand how customers are feeling, how customers are thinking. And it allows me to get often when it’s paired with qualitative feedback, even more so because I want to understand the ‘why’ and I’ll dive more into the qualitative piece, how critical is it and how often we forget that piece when we’re chasing metrics and looking for numbers, especially we’re engineers, we want numbers. We need a story and the story, you can’t get the story just from the numbers. So I love the qualitative aspect. And then the third thing I look at is, um, SCIs or failed customer interactions, trying to find friction in the journeys. What are all the times a customer tries to do something and they fail? And you know, you can define that in so many kinds of ways, but capturing that is one of those things you try to figure out. Find failed customer interactions, find where customers are hitting friction points, and let’s figure out which of those are most important to attack. So these things help guide, at the minimum, what do we need to work on as a team? Right? What are the things we need to start focusing on to deliver and build? Like, how do I get initiatives? Obviously, that stuff alone doesn’t turn into initiatives. So the next thing I like ensuring and I drive to figure out what we work on is with all my leaders. And in our organization, we don’t have separate product managers. You know, engineering leaders are product managers. They have to build those product skills because we have such a technical product that we decided to make that decision, not only for efficiency’s sake and stop having two people in every conversation, but also to build up that skill set of ‘I’m building for engineers, and I need to know my engineering product very well, but now let me enable these folks with the frameworks and methodologies, the ideas and the things that help them make product decisions.’ So, when we look at these numbers, we try to look at what are some frameworks and ways to think about what am I going to build? Which of these is going to impact? How much do we think it’s going to impact it? What level of confidence do I have in that? Does that come from the gut? Does that come from several opinions that customers tell us that, is the data telling us that, are competitors doing it? Have we run an experiment? Did we do some UX research? So the different levels of, uh, confidence in I want to do this thing. Cause this thing’s going to move that number. We believe that number is important. The FCI is it through the roof. I want to attack them. This is going to move it. Okay. How sure are you? He’s going to move it. Now, how are we going to measure that? And indeed moved it. Then I worked, so that’s the outside of the onion. Then I work inward and say, great, how good are we at getting at those things? So, uh, there’s two combinations of measures. I pull measures and data from, from GitLab, from GitHub, I look at the deployments that we have. Thankfully, we run a database. We have an OLAP database, so I can run a bunch of metrics off of all this stuff. We collect all this data from all this telemetry from our services, from our deployments, from our providers for all of the systems we use, and then we have these dashboards we built internally to track aggregates, track metrics and track them in real time, because that’s what Tinybird does. So, we use Tinybird to Tinybird while we Tinybird, which is awesome. So I, we’ve built our own back dashboards and mechanisms to track a lot of these metrics and understand a lot of these things. However, there’s a key piece which I haven’t introduced yet, but I have a lot of conversations with a lot of people on, hey, why did this number move? What’s going on? I want to get to the place that we actually introduce surveys. Funny enough, when you talk about the beginning of DORA, even today, DORA says, surveys are the best way to do this. We try to get hard data, but surveys are the best way to get it. For me, surveys really help, um, forget for a second what the numbers are telling me, how do the engineers feel? Because then I get to figure out why do you feel that way? It allows me to dive in. So that’s why I believe the qualitative subjective piece is so important to then bolster the numbers I’m seeing, either A: explain the numbers, or the other way around, when I see a story, I’m like, do the numbers back up that story? The reality is somewhere in the middle, but I use both, both of those to really help me.
Kovid Batra: Makes sense. Makes sense. Great guys. I think, uh, thank you. Thank you so much for sharing such good insights. I’m sure our audience has some questions for us, uh, so we can break in for a minute and, uh, then start the QnA.
Kovid Batra: All right. I think, uh, we have a lot of questions there, but I’m sure we are going to pick a few of them. Let’s start with the first one. That’s from Vishal. Hi Ariel, how do you, how do I decide which metrics to focus while measuring teams productivity and individual metrics? So I think the question is simple, but please go ahead.
Ariel Pérez: Um, I would start with in terms of, I would measure the core four of DORA at the minimum across the team to help me pinpoint where I need to go. I would start with that to help me pinpoint. In terms of which team productivity metrics or individual productivity metrics, I’d be very wary of trying to measure individual productivity metrics, not because we shouldn’t hold individuals accountable for what they do, not because individuals don’t also need to understand, uh, how we think about performance, how we manage that performance, but for individuals, we have to be very careful, especially in software teams. Since it’s a team sport, there’s no individual that is successful on their own, and there’s no individual that fails on their own. So if I were to think, you know, if I were to measure and try to figure out how to identify how this individual is doing, I would, I would look for at least two things. Number one, actual peer feedback. How, how do their peers think about this person? Can they depend on this person? Is this person there when they need him? Is this person causing a lot of problems? Is this person fixing a lot of problems? But I’d also look at the things, to me, for the culture I want to build, I want to measure how often is this person reviewing other people’s PRs? How often is this person sitting with other people, helping unblock them? How often is this person not coding because they’re going and working with someone else to help unblock them? I actually see that as a positive. Most frameworks will ding that person for inactivity. So I try to find the things that don’t measure activity, but are measuring that they’re doing the right things, which is teamwork. They’re actually being effective at working in a team when it comes to individuals.
Kovid Batra: Great. Thanks, Ariel. Uh, next question. That’s for you, Cesar. How easy or hard is the adoption and implementation of SEI tools like Typo? Okay, so you can share your experience, how it worked out for you.
Cesar Rodriguez: So, so two things. So, so when I was evaluating tools, um, I prefer to work with startups like Typo because they’re extremely responsive. If you go to a big company, they’re not going to be as responsive and as helpful as a startup is. They change the product to meet your expectations and they work extremely fast. So that’s the first thing. Um, the hard part of it is not about the technology itself. The technology is easy. The hard part is the people aspect of it. So you have to, if you can implement it early, uh, when your company is growing, that’s better because they’ll, when new team members come in, they already know what are the expectations and what to expect. The other thing is, um, you need to communicate effectively to your team members why are you using this tool, and getting their buy-in for measuring. Some people may not like that you’re going to be measuring their commits, their pull requests, their quality, their activity, but if you have a conversation with, with those people to make them understand the ‘why’ and how can you connect their productivity to the business outcomes, I think that goes far along. And then once you’re, once you’re in place, just listening to your engineers feedback about the tool, working with the vendor to, to modify anything to fit your company’s need, um, a lot of these tools are very cookie cutter in their approach, um, and have a set of, set of capabilities, but teams are made of people and people have different needs. So, so make sure that you capture that feedback, give it to your vendor and work with them to make the tool work for your specific individual teams.
Kovid Batra: Makes sense. Next question. That’s from, uh, Mohd Helmy Ibrahim, uh, Hi Ariel, how to make my senior management and junior implement project management software in their work, tasking to be live update tracking status update?
Ariel Pérez: Um, I, that one, I’m of two minds cause only because I see a lot of organizations who can get really far without actual sophisticated project management tooling. Like they just use, you know, Linear and that’s it. That’s all enough. Other places can’t live without, you know, a super massive, complex Jira solution with all kinds of things and all kinds of bells and whistles and reports. Um, I think the key piece here that’s important and actually it was funny enough. I was literally just having this conversation with my leadership team, my engineering leadership team. It’s this, you know, when it comes to the folks involved is do you want to spend all day asking, answering questions about where is this thing, how is this thing doing, is this thing going to finish, when is it going to finish, or do you want to just get on with your work, right? If you want to just get on with your work and actually do the work rather than talk about the work to other people who don’t understand it, if you want to find out when you want to do it, you need some level of information radiator. Information reader, radiators are critical at the minimum so that other folks can get on the same page, but also if someone comes to you and says, Hey, where is this thing? Look at the information radiator. It’s right there. You, where’s the status on the, it’s on the information radiator. When’s this going to be done? Look at the information radiator, right? That’s the key piece for me is if you don’t want to constantly answer that question, then you will, because people care about the things you’re working on. They want to know when they can sell this thing or they want to know so they can manage their dependencies. You need to have some level, some minimum level of investment of marking status, marking when you think it’s going to be done and marking how it’s going. And that’s a regular piece. Write it down. It’s so much easier to write it down than to answer that question over and over again. And if you write it down in a place that other people can see it and visualize it, even better.
Kovid Batra: Totally makes sense. All right, moving on. Uh, the next question is for Cesar from Saloni. Uh, good to see you here. I have a question around burnout. How do you address burnout or disengagement while pushing for high productivity? Oh, very relevant question, actually.
Cesar Rodriguez: Yeah, so so for this one, I actually use Typo as well. Um, so Typo has this gauge to, um, that tells you based on the data that it’s collecting, whether somebody is working higher than expected or lower than expected. And it gives you an alert saying, hey, this person may be prone to burnout or this person is burning out. Um, so I use that gauge to detect how is the team doing and it’s always about having a conversation with the individual and seeing what’s going on with their lives. There may be, uh, work things that are impacting their productivity. There may be things that are outside of work that are impacting that individual’s productivity. So you have to work around that. Um, we are, uh, it’s all about people in the end, um, and working with them, setting the right expectations and at the same time being accommodating if they’re experiencing burnout.
Kovid Batra: Cool. I think, uh, more than myself, uh, you have promoted Typo a lot today. Great, but glad to know that the tool is really helping you and your team. Yeah. Next question. Uh, this one is again for you, Cesar from Nisha. Uh, how do you encourage accountability without micromanaging your team?
Cesar Rodriguez: I think, I think Ariel answered this question and I take this approach even with my kids. Um, it’s not about telling them what to do. It’s about listening and helping them learn and come to the same conclusion as you’re coming to without forcing your way into it. So, so yeah, you have to listen to everybody, listen to your stakeholders, listen to your team, and then help them and drive a conversation that can point them in the right direction without forcing them or giving them the answer which is, which requires a lot of tact.
Ariel Pérez: One more thing I’ll add to that, right, is, you know, so that folks don’t forget and think that, you know, we’re copping out and saying, hold on, what’s your job as a leader? What are you accountable for? Right? In that part, there’s also like, our job is let them know what’s important. It’s our job to tell them what is the most important thing, what is the most important thing now, what is the most important thing long term, and repeat that ad hominem until they make fun of you for it, but they need to understand what’s the most important, what’s the strategy, so you need to provide context, because there’s a piece of, it’s almost like, it’s unfair, and it’s actually, I think, very, um, it’s a very negative thing to say, go figure it out, without telling them, hold on, figure what out? So that’s a key piece there as well, right? It’s you, you’re accountable as the leader for telling them what’s important, letting them understand why this is important, providing context.
Kovid Batra: Totally. All right. Next one. This one’s for you, Cesar. According to you, what are the most common misconceptions about engineering productivity? How do you address them?
Cesar Rodriguez: So, so I think the, for me, the biggest thing is people try to come with all these new words, DORA, SPACE, uh, whatever latest and greatest thing is. Um, the biggest thing is that, uh, there’s not going to be a cookie cutter approach. You have to take what works from those frameworks to your specific team in your specific situation of your business right now. And then from there, you have to look at the data and adapt as your team and as your business is evolving. So that’s, that’s the biggest. misconception for me. Um, you can take, you can learn a lot from the things that are out there, but always keep in mind that, um, you have to put that into the context of your current situation.
Kovid Batra: I think, uh, Ariel, I would like to hear you on this one too.
Ariel Pérez: Yeah. Uh, definitely. Um, I think for me, one of the most common misconceptions about engineering productivity as a whole is this idea that engineering is like manufacturing. And for so long, we’ve applied so many ideas around, look, engineering is all about shipping more code because just like in a fan of factory, let’s get really good at shipping code and we’re going to be great. That’s how you measure productivity. Ship more code, just like ship more widgets. How many widgets can I ship per, per hour? That’s a great measure of engineering productivity in a factory. It’s a horrible measure of productivity in engineering. And that’s because many people, you know, don’t realize that engineering productivity and engineering in particular, and I’m gonna talk development, as a piece of development, is it’s more R&D than it is like doing things than it’s actual shipping things. Software development is 99% research and development, 1% actually coding the thing. And if they want any more proof of that is if you have an engineer working on something or a team working on something for three weeks and somehow it all disappears and they lose all of it, how long will it take them to recode the same thing? They’ll probably recode the same thing in about a day. So that tells you that most of those three weeks was figuring out the right thing, the right solution, the right piece, and then the last piece was just coding it. So I think for me, that’s the big misconception about engineering productivity, that it has anything to do with manufacturing. No, it has everything to do with R&D. So if we want to understand how to better measure engineering productivity, look at industries where R&D is a very, very heavy piece of what they do. How do they measure productivity? How did they think about productivity of their R&D efforts?
Kovid Batra: Cool. Interesting. All right. I think with that, uh, we come to the end of this session. Before we part, uh, I would like to thank both of you for making this session so interesting, so insightful for all of us. And thanks to the audience for bringing up such nice questions. Uh, so finally, before we part, uh, Ariel, Cesar, anything you would say as parting thoughts?
Ariel Pérez: Cesar, you wanna go first?
Cesar Rodriguez: No, no, um, no, no parting thoughts here. Feel free to, anyone that wants to chat more, feel free to hit me up on LinkedIn. Check out stackgen.com if you want to learn about what we do there.
Ariel Pérez: Awesome. Um, for me, uh, in terms of parting thoughts is; and this is just because how I’ve personally thought about this is, um, I think if you lean on the figuring out what makes people tick and figure, and you’re trying to take your job from the perspective of how do I improve people, how to enrich people’s lives, how do I make them better at what they do every day? If you take it from that perspective, I don’t think you can ever go wrong. If you make your people super happy and engaged and they want to be here and you’re constantly motivating them, building them and growing them, as a consequence, the productivity, the outputs, the outcomes, all that stuff will come. I firmly believe that. I’ve seen it. I firmly believe it. It really, it would be really hard to argue that with some folks, but I firmly believe it. So that’s my parting thoughts, focus on the people and what makes them tick and what makes them work, everything else will fall into place. And if I, you know, just like Cesar, I can’t walk away without plugging Tinybird. Tinybird is, you know, data infrastructure for software teams. You want to go faster, you want to be more productive, you want to ship solutions faster and for the customers, Tinybird is, is built for that. It helps engineering teams build solutions over analytical data faster than anyone else without adding more people. You can keep your team smaller for longer because Tinybird helps you get that efficiency, that productivity out there.
Kovid Batra: Great. Thank you so much guys and all the best for your ventures and for the efforts that you’re doing. Uh, we’ll see you soon again. Thank you.
Cesar Rodriguez: Thanks, Kovid.
Ariel Pérez: Thank you very much. Bye bye.
Cesar Rodriguez: Thank you. Bye!
In this episode of the groCTO Podcast, host Kovid Batra interviews Anton Zaides, the Director of Engineering at Taranis and author of the Leading Developers newsletter. Their discussion focuses on the challenges and strategies involved in leading development teams versus platform teams.
He recounts how his early interest in gaming and experiences as a guild master in World of Warcraft shaped his leadership style, teaching him valuable lessons in social intelligence and teamwork. Maher outlines his proprietary framework for peak performance focusing on shared understanding, trust, and competence, and highlights the significant benefits of leveraging generative AI tools like GitHub Copilot for improving productivity. The episode also delves into the complexities of implementing new technologies and managing distributed teams, underscoring Maher’s strategies for overcoming these challenges through continuous learning and fostering a collaborative culture.
Kovid Batra: Hi everyone. This is Kovid, back with another episode of groCTO by Typo. And today with us, we have a very special guest who is coming to the show for the second time, but first time for this year. That’s Anton. Welcome to the show, Anton.
Anton Zaides: Thank you, Kovid. Great to be back.
Kovid Batra: So let me introduce Anton. Uh, so Anton, guys, is Director of Engineering at Taranis, a company from Tel Aviv. And, uh, he is also the author of Leading Developers, which is a trending newsletter, at least on my list. And he is having almost 18,000 subscribers there, writing some great articles we are really fond of at groCTO. So congratulations to that, Anton, and welcome to the show again.
Anton Zaides: Thank you so much.
Kovid Batra: All right. Uh, so today’s topic of discussion is one of the topics from Anton’s newsletter, which is ‘Leading Dev Teams Vs Platform Teams’. This was a very interesting topic. Uh, I read the whole newsletter, Anton, and I really found it very interesting and that’s the reason I pulled you off here. And, uh, before we like jump into this, I’m really curious to ask you a few questions about it. But before that, I just want to know, uh, how was your last year? How did 2024 go? What are your plans for 2025? So that we get to know a little more about you.
Anton Zaides: So 24 was very busy. I had my, uh, I had my first kid at the beginning of the year, so a year ago, and got promoted a month after that. So it was a year full of..
Kovid Batra: Super hectic.
Anton Zaides: Yeah! Hectic, career, family, and I think a small one would be in my, uh, first international conference, uh, back in September, which was a great experience for me, you know, like talking in English with an audience. So I would say lot of family, lot of career. And in the next year it’s more about family. I’m right now taking a 7–8 months break and I’m planning to work on my own thing. Early child education, mainly helping parents, children, like my own kid’s age. Just a bit of technology and also learn about it. You know, I feel parents don’t really know what they’re doing. So that’s my goal for next year, to be a better father and use technology for that.
Kovid Batra: No, that’s really amazing. I know this is, I think there are a few experiences in a human’s life and this is one of those which changes you completely. And, and in a, in a very good way, actually. Uh, when you’re young, you usually do not love to take responsibilities. Nobody loves to do that. But when such kind of responsibilities come in, uh, I think you, you grow as a person, there is something that, uh, something else that you explore in your life, at least I would, I’ve seen, uh, in my friend circle and of course, I can relate to what you’re saying also. So, congratulations and all the best. Uh, we really feel that you would do great here as well.
Anton Zaides: Thank you. Thank you. Definitely. We’ll try.
Kovid Batra: Yeah. All right, Anton, uh, coming to the main section, uh, talking about platform teams and dev teams, uh, this topic is very unique in, uh, in a way that nobody usually gets to talk about it in detail, in depth the way you have done it. Of course, a lot of generic articles are there. I’ve read a lot. This session could be a really good guide for someone who is, uh, in a position where they are moving into these roles from, uh, leading dev teams to platform teams. They could really have some learnings from what you have experienced in the past. So, first question to you actually, why did this topic come to you? What happened in your personal experience that made you realize that, okay, this could be something that an engineering manager or a tech lead who is switching between these kind of responsibilities would be interested in knowing?
Anton Zaides: Going back, I first started in a classic dev team, right? I wrote code like everyone else for a few years, and then I switched to the platform side, DevOps side, more infrastructure, and led the team there for a couple of years. And I decided to switch back. So it was two switches I did. And in my last role as an engineering manager of a classic product-facing, you know, user-facing team, I felt that most of the other engineering managers in the organization, they don’t really know how to work with the platform team. We have a DevOps platform team that provide us, you know, all the tools, they help us, and I felt they don’t really understand, uh, how to approach them, how to help them, how to connect them to the business. So they just really liked working with my team and I always got what I wanted and I pushed the agenda for that. And it really, really helped my developers too, right? Because they got close to the platform developers and they understood it better, that made them better developers. And I felt like this connection can help other engineering managers who never experienced how difficult it is to be in a platform or DevOps team. I’m using the terms, uh, interchangeably, but, uh, let’s call them platform for now. So I felt that, you know, I can show the other side and I hope it will help other engineering managers to see the difficulties and stop being annoying, because, you know, we are the, we are the clients. It’s very, very hard to satisfy developers for platform teams. It’s almost impossible. You’re always too slow. You’re always like, too many bugs. You’re always not prioritizing me enough. So I wanted to show a bit of the other side. So that was the focus of the article, like showing the inside of a DevOps team with some tips, product teams on how to help the, those DevOps teams. That was the idea.
Kovid Batra: Hmm. Interesting. Interesting. So this was some real pain coming out there and like you telling people, okay, this is what the picture is so that they know what’s going on. Right. I think that makes a lot of sense. And I think a lot of people connected to that. And even I like the article a lot. Um, I was reading one section, uh, from the article, which mentions about, like this is something which is really, really hard to manage, right? Uh, because the, the expectations are very hard and you just now mentioned about, uh, it’s, it’s very hard to satisfy the developers and then the requirements are changing too fast. So these were the first two things I remember from your article which were, you, you touched base upon. So can you just give me some examples and the audience about how you see things are changing really fast or how it is becoming very difficult for you to manage these demanding clients, actually?
Anton Zaides: First of all, I think when your clients are technical and they are inside the company, they feel the privilege to tell you how to do things and prioritize your work, right? Because they say, Oh, why does it take you a month? So, I know I can do it for a week, right? They feel they can do the platform work and they kind of push the platform teams. Um, I had an example where when I was doing the platform team, we were responsible for, I don’t want to get too technical, but we had, uh, you know, database services like Postgres, MongoDB, Redis, right? Storage databases. So we were in a private cloud and we were responsible for, uh, providing those database as a service. What do you have in AWS and GCP? You just can request one. So we needed to do the same in our own private cloud, which is quite complex. And we provided PostgreSQL and MongoDB and Redis. And every day another developer says like, why don’t you do Cassandra? Or why don’t you do CouchDB? Like they felt like they know what needs to be done and they didn’t. They never thought, you know, in my opinion, Postgres is perfect for 99.9% of the startups and their products, but the developers felt like they need to push me to provide them new database just because they wanted to use new technologies, right? And now I heard like, uh, for example, we have Jenkins, right? So in my company, I heard developers complain, why Jenkins? It’s so slow. We need to replace it for something faster. Right. And this is something as a product team, you’ll never hear your client tell you, why do you use React? You need to use Vue. Right? It’s faster. It’s, they don’t care, right? They care about the end result. And here the comments like this, like does somebody really know how hard it is to replace Jenkins with another tool? What are the costs? What are the benefits? Why do it? So So they feel very comfortable, like, suggesting and giving their opinion, even if nobody really asks them, I would say. That’s one thing.
And the other one about the priorities is it’s actually, I would say sense of urgency that there are a lot more fires in the platform teams. For example, if you have, uh, we had the case of a GPU problem, right? You know, the world has, uh, not enough GPUs. So we had, we use, uh, the cheaper version of GPUs where they don’t promise you enough. And then we had a bottleneck and we needed the GPUs, but we couldn’t get them. And now we needed to change all the infrastructure to request the higher GPUs and kind of balance them to save prices. And this is a project that took one month and it’s completely stopped what they’re working on, which was also important. And you have so many incoming things like that, you know, you have an alert somewhere, right? Something is crashing. Very often it’s the developer’s problem. But if you see, uh, prod crashing, you say, okay, it’s, it’s the DevOps. They don’t have enough memory or they don’t have enough nodes or something like that. And then you kind of need to debug and then you understand it’s the developer’s problem. You tell them and then they debug and come back to you because they don’t do their job well. So this all back and forth makes it very, very, very hard to concentrate. I remember sitting in, you know, you have this tap on the shoulder, “Please help me a bit. Uh, please explain to me why this is not working.” Uh, clients usually in a product team, you have customer support, you have customer success. You have so many layers that isolate the developers from distractions, right? And you can see it straight here. Your clients are sitting by your side and they just go over and sit by you expecting you to help them. I think product developers would have been crazy if your client would come up to you and say, “Oh, this. I see an error, help fix it now.” So, yeah, I agree. Those are the two things that, that make it, uh, very hard, clients being opinionated and always distracted.
Kovid Batra: Right. I think from the two points that you mentioned, uh, there is always unwanted suggestions, recommendations, and then there is, there is this explanation when you do not want to be directly interacting with them, there should be a first level of curation on whether the problem belongs to the platform team or to the developer, there should be some level of clarity there and then probably there should be deep diving into what’s going on, who’s responsible. So what I felt is, let’s say just hypothetically, uh, five years down the line, you are an engineering leader who is managing the complete tech for, for an org. Uh, you have platform team, you have your development team, right? What advice or what kind of culture you would like to set in? Because it seems like a problem of a culture or perception where people like blame the platform teams or do not empathize with the platform teams that much. So, as an engineering leader down the line who is leading two different teams, what kind of culture you would like to set in or what kind of practices you would want to set in so that platform teams who are equally critical and responsible and accountable for things as development teams are operating neck to neck? Or I’m not, I’m short of words here, but I hope you get the sense of what I’m trying to say.
Anton Zaides: Yeah, I think I got it and, it’s, it’s a small thing that we’ve actually tried, but I think if I would have been the decision maker to be on a biggest scale, actually to switch places for at least a while. So I believe that platform and DevOps knowledge is super useful for every engineer, right? Not always the other way around. So I truly believe that every product engineer should know about platform, at least the basics, not every platform engineer should know React, right? Depends on what they work in, but I would put the product engineers and put them for a month, uh, helping the platform teams in a project. Like, everyone should do a bit of platform work to understand, to see how they work, right? They can work in Kanban and not your usual scrum to see how they’re day to day. If you see from the other side, like if you need to provide support to your own team, right, you are the pipeline. You will see how many requests are coming through and the other way around. I feel that we had, uh, for two sprints, like for a month, we had one of the platform developers in our team because he wanted to experience the life of a developer to understand the problem better and the usage of his own systems. And it was really, really mind opening for him too, to understand why we complain, what he thought was so easy to understand that it’s our problem. Once he sat with us and tried and developed and, uh, released some backend code to production and understood it’s not that easy. And so this connection of switching places and it has some cost, but I feel it’s worth it.
And the second one I would say is connect, like the road map shouldn’t be different, right? They should be much more connected. So when you’re building the platform roadmap, you should have, of course, the engineering managers, but not only when you build it. Like, they should be there at every release kickoff, every, every time they should be part of the platform roadmap. This is the easy part. The harder part is to explain to the platform people the your product, right, how is your 3–4 months going to look? What are you working on? What do you expect? And not just the managers, which is what usually happens, right? You have a manager sitting with a manager, discussing and stuff like that. The people underneath need to understand that, uh, sit there. For example, a platform engineer should hear customer success stories that he indirectly helped because a big part of the problem that when you work in the platform team, you don’t really affect the business bottom line, right? You help developers create solutions, but if you can have those stories of how you helped someone deliver something faster and what was the impact on the company, it creates like a shared responsibility because next time you will want to help them faster. You will want to understand the problem better because you feel the impact. Saying, “I released the service to production in five minutes instead of three hours.” That’s nice. But saying, “I released the feature a week earlier and a bigger deal was, uh, agreed by the customer because of the DevOps team.” Right? Doing this connection. It’s not always easy, but in a couple of cases, we were able to do that connection. Um, platform work directly to business outcomes. I feel that would be something that we try, uh, much more. Um, so yeah, if I had to choose one, it’s just, uh, switching the places a bit, we had a concept called ‘DevOps Champions’, but it can be ‘Platform Champions’, uh, where you pick one developer from each product team and they have a weekly meeting with the platform team and like hear about the latest news, ask questions. And for example, they are the point of contact before you can contact the platform team. You have someone in your team who is interested in platform and he gets more, uh, he gets like, I would say Slack, direct Slack access to the DevOps team They know like this person, if you ask, we will drop everything and help them. And they, they do trust. And then the whole team talks to one person instead of to the DevOps team. And, and this helps a bit. So I hope it was not too confusing. So if I sum it up, I say switch places and have a dedicated platform, uh, representative inside the product teams and also connect the platform team to the business side. Yeah.
Kovid Batra: That really makes sense. Uh, this point which you mentioned about bringing DevOps Champions, right? Like who are going to be the point of contact for the product teams to share knowledge, understand things. Going back to your newsletter, uh, you mentioned about bringing more visibility and recognition also. So is this dev champs, DevOps Champions some way of recognition also that you want to bring in into the teams to have a better culture there? I mean, basically these teams lack that level of recognition just because they’re not, again, directly impacting the business. So they don’t really get to see or feel what exactly they have done is, is this an outcome or consequence of that?
Anton Zaides: No, I think it’s a bit different because the champions are product engineers, like who are originally from inside the team. So if I have five developers, one of them will be like, uh, will wear the platform hat, but he will be a product engineer and he will get to, to, uh, learn from them and work with them, the ones who are interested. For the recognition, I’m talking about recognition of the pure platform engineers, which are usually in the dark and separate there. And there it’s about what we, we discussed a bit earlier, also sharing their stories, but also public acknowledgement. That’s something that I really, I have the privilege of having a LinkedIn, you know, and I constantly write there. So I, I did a couple of shoutouts for our platform engineers after nice projects, and they really, really appreciated it because, you know, people usually, you know how it is. If it works, they don’t hear about platform, only when it breaks. So they don’t get like kudos for nice projects and stuff like that. So I really try both on LinkedIn, but also in internal companies like channels, you know, saying nice words, uh, appreciating the work, stuff like that.
Kovid Batra: Makes sense. Makes sense. Totally. I think, uh, one thing I would be interested in knowing, like any of the projects that you took up as a platform team lead and completed that project. What was the mindset, what was the need, uh, and then how you accomplished it? Just deep diving into that process of being a platform team lead, uh, leading a project to make the lives of your developers, uh, better and maybe making them more productive, maybe delivering faster.
Anton Zaides: So let me think, it’s been a while, right? It’s four or five years ago since I was there. But I think if I go back, right, my team’s role was to deliver database as a service for our customers, right? Customers and developers, they want, uh, whatever PostgreSQL, uh, MongoDB and they, it’s hard for me to explain to people how it is without a public cloud. I was in a government agency, so there was no GCP, AWS, Azure. It was like everything, you need to create everything. It was an air gapped environment. Because of, you know..
Kovid Batra: Uh, information, regulation.
Anton Zaides: Regulation, information, you couldn’t use stuff like that. So we need to do everything from, from scratch. And one thing that, uh, we were a small team, so all the communication was, uh, we didn’t have like a portal, right? I know it’s very hard to imagine a world without the public cloud, but it was like emails and messages, please create me a database and stuff like that. And one very small annoying thing was the extensions and Postgres. You have many default extensions, like you have PostGIS, like for geographic extensions, you have like, uh, for using it as a vector database, you have many extensions, and we wanted to help them use those extensions, right? Because every time they needed a new extension, they need to send us an email. We need to check it. We needed to roll it out and stuff like that. So I know it’s, I think it’s not what ideally what you, uh, meant because it was quite a small project, but I saw that pain and we kind of went and figured out the top 20–30 extensions that did some templates and did some UI work, which is quite rare for platform teams, right? Because you hate UI, usually if you’re in platform. At most, you can do some backend, but you prefer to do like, you know, flash scripts and stuff. So we did some basic, uh, interface with React, HTML, CSS. So to create this very ugly portal, which I think people appreciated. It makes the work easier. And I think the good, the good platform teams are not afraid of writing a bit of code and using like graphical interface to a small portal or like, uh, if you want to request to see stuff like that instead of waiting for product teams to help them create a nice screen and stuff like that. Now with Cursor and, you know, and all the LLM, it can take you 30 minutes to do everything you need. Like, you have APIs, you can put them where they can use buttons to do like that, you need to request something. So I think like that barrier, if I go back to the story to break the barrier and not say, okay, I can only do backend stuff. That’s how it works. I will. And just think about the next step and go where it’s, it’s uncomfortable. I had, I was lucky because I had the background as a product developer, so it’s easy for me. But all of my team members, there was like, no, no way we’re going to write React. No, it’s not our job and stuff like that. So I had to, to force them a bit, force them and I actually enjoyed it because you know, it’s It’s, it’s rarely in the platform that you can actually see something immediately
Kovid Batra: This was an interesting experience and how this experience would have changed in case of such kind of requirement when it comes to dev teams, like, because we are just comparing like a while leading dev teams is different from leading platform teams. So in this situation, of course, there was a barrier. Uh, there was a problem which the platform teams had to solve, but it came with a solution that platform teams are usually not inclined towards like building the UI, right? If a similar kind of a situation had to come in for the dev teams, how do you think it would have been easier or difficult for you to manage as a manager?
Anton Zaides: I would say as a dev team, you have a product manager, you have UX designers, and you get a ready Figma of how it should look like, and you just implement it in, in a couple of days, right? It’s so much easier because someone is doing the research of talking to the customers. Some platform teams have a product manager, right? I would not say, but they for sure don’t have a UX designer working with them, because the system is internals and everybody say, “Oh, just make it good enough. Uh, these are our people anyway. You don’t need to make it beautiful.” So this, this is usually how it works. And in the product team, for me as a manager, it’s so much, much less work for me. The product manager, uh, doing most of the work. And I would just like, you know, manage the people a bit, coach them. But as a platform team, I did it, like 50% of my job I did product management. For some of the time I did have a dedicated product manager, but some of that I didn’t and I needed to kind of fill the hole myself. Yeah, because in platform team, it’s the first team where you cut the product manager. You say, “Oh, it’s internal. No need. Uh, the engineering manager can manage.”
Kovid Batra: That’s even my point, yeah. So even I, I felt so, like for platform teams, do you think it is even important to have a product manager? Because the tech lead or probably the engineering manager who’s involved with the team would be a good start to make sure like things are falling in the right places and understanding the problem. See, ultimately for a product manager, it is very important to be more empathetic towards the client’s problems and be able to relate to it. The more they relate, The more fit is there, the better solutioning they can design. Right. Similarly for an engineering manager who is leading the platform team, it would be more of a product role and it makes more sense also, as per my understanding. What do you have to say about that?
Anton Zaides: I have had experience with product managers with platform team who didn’t come from an engineering background and it was always a failure in my experience. Uh, I would say it’s better to have no product manager to let the engineering manager do the job. And ideally in, in that team after, I think it was after a year and a half, one of the engineers, like she mentioned she wants to become a product manager. This is her career path and then it’s a perfect fit, right? If you have an engineer who wants to become a product manager from inside the company, then it can work great. But I feel that in the platform case, the product manager must have an engineering background. Otherwise, like you can try to learn to be technical, but it would just be, it would be a different language. It would be, it’s not like product teams. Yeah, I agree. I feel it’s, uh, yeah, it just doesn’t work in my experience.
Kovid Batra: Makes sense. By leading a platform team where you find this kind of a fit where some engineer who is interested in becoming a product manager comes in and plays a role, I think I sense that there is definitely a need of a person who understands the pain, whether that person is an engineer or the engineering manager who is working as a product manager, but you definitely need that kind of a support in the system to make sure that requirements are flowing in correctly, right?
Anton Zaides: Yeah, I agree.
Kovid Batra: And most of the time what I have seen or felt is that engineers usually shy away or the engineering team shies away from being involved that aggressively towards client requirements. So when it comes to platform teams, how do you bring that extra level of empathy towards customer problems? Of course, they are developers, they relate to the problem, but still, I feel that in a world where we live dealing with real world problems, being a developer, you still get to see some side of it because you’re a human, you’re living in the, in that world. But when it comes to platform teams, it’s all technical. You have seen things, but still, it’s more like you are just solving a technical problem. So the empathy towards deep diving into the problem and bringing up a solution, does it become harder or easier when you are raising a product manager in an engineering team for platform teams?
Anton Zaides: I think it’s quite hard and I think this is the role of the engineering manager, of the platform engineering manager. Like I feel the product managers still have difficulty bridging that gap. I would say that platform engineers, either by experience or by character, they care more about the technical side. You know this term of product engineer, which is like pure product engineer, not like software engineer, like the people who decide what to build. Platform engineers, from my experience, care about the technical side, like much, much more, right? They want to build excellent solutions, they are excited by crazy bugs and they are excited by saving costs, stuff that most people are less excited by that. And yeah, it’s, it’s purely the job of the engineering manager. Like, as a platform manager, you need to show the pains of the developers too. That’s much more than in a product team where the PM filled that gap. I feel that even if a PM is an ex-engineer, in my experience, somehow, like, if the engineering manager won’t do it, the developers will resist much more the PM. Right? I think that’s what comes to mind. You have much more resistance in the platform team because they want to stay in the code. They don’t want to join customer meetings. They don’t want those things. Just want to code. So you need to, you know, like, uh, peel the shell and try to bring developers to share their stories, send them for a month for a development team, as we discussed, which they will hate probably. So you need to, to, push a bit. And the PM, it’s not, they are not his or her direct report. So they have limited power and you can actually, I would not say force, but kind of help them hardly along that path, uh, of understanding the user brains. Yeah.
Kovid Batra: Great, Anton. I think, um, thanks. Thanks for this interesting talk and helping us deep dive into the platform teams and the dev teams and how they differ in their core DNA. Uh, I think there were some great insights about how things change when you are leading a platform team, that from the expectations, from the kind of mindset that the developers come with, the unwanted suggestions, and like how you bring more connectedness to the business and recognizing teams. So I think this was a very interesting talk. Before we moved from the session, uh, is there any advice, uh, parting advice that you would like to give to the audience?
Anton Zaides: My main advice would be to the product leaders, product engineering managers to try much harder to understand the pain of the platform teams in your organization and how can you help them. Schedule 1-on-1s with the platform engineering manager, be more involved because they will appreciate that help and they might not even know they need your help. And in my experience, you will benefit for sure.
Kovid Batra: Makes sense. Makes sense. I think this would not only help reducing the friction, but will also help, uh, in bringing a better and a collaborative effort to build better product also like better platforms also.
Anton Zaides: For sure.
Kovid Batra: Great, Anton. Thank you. Thank you so much once again, uh, it was great having you on the show. Thank you.
Anton Zaides: Thank you, Kovid. It was great being here.
As engineering leaders, we often get caught between high-level business goals and the day-to-day delivery trenches. In this candid conversation with Maher Hanafi, VP of Engineering at BetterWorks, we unpack how to scale engineering productivity without sacrificing team autonomy. Whether you lead a team of 10 or 200, you’ll find frameworks that help align engineering output with business outcomes—without micromanagement.
He recounts how his early interest in gaming and experiences as a guild master in World of Warcraft shaped his leadership style, teaching him valuable lessons in social intelligence and teamwork. Maher outlines his proprietary framework for peak performance focusing on shared understanding, trust, and competence, DORA metrics and highlights the significant benefits of leveraging generative AI tools like GitHub Copilot for improving productivity. The episode also delves into the complexities of implementing new technologies and managing distributed teams, underscoring Maher's strategies for overcoming these challenges through continuous learning and fostering a collaborative culture.
Kovid Batra: Hi, everyone. Welcome back to groCTO by Typo. Uh, this is Kovid, your host, wishing you all a very, very happy new year. Today, we are kicking off this year’s groCTO Podcast journey with the first episode of 2025, hoping to make it even better, even more insightful for all the listeners out there. And today, for the first episode, uh, we have our special guest, Maher Hanafi. He’s VP of Engineering at Betterworks, comes with 16 plus years of engineering and leadership experience. Welcome to the show, Maher.
Maher Hanafi: Thank you, Kovid. Thank you for having me and happy new year.
Kovid Batra: Same to you, man. All right. Uh, so, Maher, uh, today we are going to talk about some engineering productivity hacks from a VP’s perspective. But before we jump onto our main discussion, uh, I think there is a lot to know about you. And to start off, uh, we would like to know something about you that your resume or your LinkedIn profile doesn’t tell. Something from your childhood, which was very eventful and then defines you today. So would you, would you like to take the stage and tell us about yourself?
Maher Hanafi: Well, that’s a great way to start the conversation. Thank you for asking this. Um, yeah, it’s not something that is on my resume and in my bio, but um people who know me know this. So I’m into gaming and I used to play video games a lot when I was a kid, to the point that I wanted my career to, to be in gaming. So I have a telecommunication background, engineering background. And then, as soon as I finished that, and I was ready to go to the market to start working, I decided to completely go and pursue a career in gaming. So what I did is, um, I looked into the gaming job, game developer jobs, and I figured out everything they’d need to, um, to have, to be had as a game developer. And I learned that. I taught myself these things and two years later I was working for Electronic Arts. So a great story there is like this passion I had as a kid for many years led me to, um, go into and pursue that career. Another part of that same story, as a gamer, I used to play a lot of, uh, massive multiplayer online video games, like MMOs. Uh, one of the biggest one is World of Warcraft, and at that time, I used to play the game a lot to the point that I was a guild master, meaning I was leading a big team, uh, hundreds of people, um, telling them, you know, kind of a leadership position. So in other words, I was a manager, uh, before I even started my career as an, as an engineer, or, uh, before I became an Engineering Manager later. So that taught me a lot of things from, you know, social intelligence and how you manage people and how you hire and fire and kind of manage productivity and performance, which will be the topic of today. So happy to be going to that later in a moment.
Kovid Batra: Oh, that’s very, very interesting. So I think, uh, before you even started off your leadership journey, you, you were actually leading a team. Though it was just gamers, but still it must have taught you a lot.
Maher Hanafi: Absolutely. Yeah, I learned a lot and I’m so grateful to that experience and a lot of what I did there are things that I brought to my career and I used as a, as a manager, um, to, to get to the engineering level.
Kovid Batra: Perfect. Perfect. I think it’s time. Let’s, let’s move on to something, uh, which is around the topic. And before, before, again, we jump onto that, uh, tell us something about Betterworks, your role and responsibility as a VP of Engineering over there. How is it like at Betterworks?
Maher Hanafi: Yeah. So, Betterworks, we are an enterprise, uh, SaaS company. So we develop an enterprise performance management software for global big companies, all the tools and suite of tools they need to manage performance internally, uh, for big companies. Again, this is more challenging when you have a, you know, departments and team and business units, and like you’re just globally distributed. Managing performance in general is very challenging. So we build and provide all these tools for, for our big customers. I’m currently the VP of Engineering. I lead all our engineering teams. Uh, we’re split between India and the US, and yeah, uh, I do different things. I, obviously, lead the technical perspective from a vision and strategy and architecture, help the team make the right decisions, build the right software, and also I contribute a lot to our strategy over time and vision, including AI. So this was one of the most recent, you know, kind of areas of focus of mine to help the team and the company deliver generative AI integrations and features and hand feature on top of what we offer, which is obviously very, very kind of important these days to be on top of that and deliver. So that’s what I do. And again, as a VP of Engineering, there’s a lot of things that get into that, including, you know, managing the team, managing productivity, ensuring that everything is being efficient and effective in having an impact.
Kovid Batra: Talking about productivity and efficiency, I think, um, I was just stalking your profile and like, I was stalking you on LinkedIn and I realized like, you have had this good journey from being a developer and then manager and then leader, right? I would want to understand how your perspective towards improving team efficiency and team productivity has changed while you were working as a manager and now working as a VP, like how, how your perspective has changed?
Maher Hanafi: Yeah. I mean, working as a, you know, going from an IC to a manager is one thing, is like going from this, you hear this a lot, going from being a player to being a coach, maybe captain/coach. So you have your scope, which is small. Usually you have your team, which is also usually small. The areas of expertise in terms of like stack and technology is also small most of the time. So when I started my journey as a manager, I was managing mobile teams and mobile development teams. So that was my area of expertise when I turned into management. But then when you get into more like senior management and the Director of Engineering and VP of Engineering, you, your scope is growing and you will be turned more horizontal than vertical, right? Like your depth of expertise gets kind of, uh, get to a certain level where you cannot go any deeper if you want to manage bigger teams. And add to that, you get involved into managing managers and you become like a coach of coaches. So the whole dynamics change over time and your areas of focus change and you become less hands-on, less technical, but still you need to keep up with things that are happening. If you go online and search for VP of Engineering, you’ll find a lot of people saying that VP of Engineering is like the hardest job in the engineering technology stack or all the roles because it has this challenge of going horizontal, trying to be as vertical as possible, managing managers and managing performance and again, focus on impact. So I think the mindset, the way my mindset changed over time is I needed to let go some of my biggest passions when, you know, I used to code and I used to go deeper into little details and very specific stacks and go more horizontal, but keep myself really up to date with things, so I can go and speak to my teams, their language and help them move the needle or what with what they do and still be a someone who can bring a vision that everyone can stand behind. So it’s a completely different game over time, but it’s organic, you know, you cannot just hop on overnight to into a new role like this and just expect yourself to be successful. So there’s a lot of learning, a lot of education You need to keep up with everything that is happening as much as you can obviously And then help your team execute and find the gaps in your own set of skills, technical, non-technical skills to be the best VP of Engineering you can to help your team proceed.
Kovid Batra: So if I have to ask about one more, like one of the hardest things for you, when you had to change yourself and you moved into this role, what was it?
Maher Hanafi: I think, definitely, going very horizontal because I think when I turned more into senior leadership positions in engineering management, I found myself very quickly into completely outside of my comfort zone, right? Like I used to do, you know, I started with gaming, obviously, that was my area of expertise. And then I learned mobile, which was a passion of mine. And then I was, that was my space. I was very comfortable there. I can do anything. I can be very efficient and I can lead a team to deliver on these areas. But then overnight, you take over, you know, web development and backend technologies and then cloud native, you know, distribution systems. So overnight you find yourself completely outside of the zone where you’re very comfortable and your team is looking up for you to guide sometimes, right? And it’s very hard for you to do any of that if you are able to speak the language to catch up with these technologies, to be someone people can stand behind in terms of like, uh, trust in terms of guidance. So that’s the moment where I felt like, “Oh, this is not the, this is not a thing I can keep doing the same way I used to do other things before. Now I need to get myself into continuous learning more proactively even ahead, you know, going a little bit ahead of my initial plans and managing teams.” So, very quickly I turn on, “Okay, what is web development? What are the key areas and components and technology stacks? How can I manage a team that does that? How can I learn back end very quickly? How can I learn infrastructure and data and then QA and security and all of that?” So as you go into these roles, again, your scope is going to grow, you know, significantly, and you need to catch up with these technologies, again, to a certain level of depth. I cannot go as deep as I went into mobile and into other technologies I was very hands-on in, but you need to have that level of depth that is good enough to drive these teams to really be a source of trust and confidence and people can stand with you as a leader, and again, be productive and perform.
Kovid Batra: Right. I think that makes a lot of sense, actually. But the thing is, like, when you are in that dilemma that how, whether you should go vertically deep into the topic or you have a responsibility to like, go horizontal as well, how do you take that call, “Okay, this is where I have to stop”, and like “This is how I would be guiding my team.”? Because when you’re talking to technologists and specifically in your case you were coming from a mobile and then a gaming background and then you took up other technologies. Anyone who is expecting some guidance there would be much deeper into that technology. So what would be that situation? Let’s say, I am that person who has technically, probably spent three, four years already in web development and you have come in as a VP and you’re trying to have a conversation with me and telling me that, okay, this is how you should be taking up things. Don’t you think that I would be the person who already knows more hands-on than you? And then in that situation, how could you guide me better?
Maher Hanafi: Well, that’s, that’s where a mix of soft skills and hard skills get into the game. And that’s where you can get into the VP of Engineering role is to be smart and socially capable of navigating these situations, right? So first of all, all the hard skills, as I said, you need to go and learn the minimum to be able to speak the language. You cannot go to, again, back end engineers and start telling them things and telling them stories about your front end engineering background. It doesn’t work. So you need to get to a certain level of learning and efficiency in the stack and the technology to be able to at least speak at a high level. And then, the other thing is where the soft skills get into the game. You need to be vulnerable. You need to be very clear about your level of expertise. You need to highlight your team members as the experts and create this environment of collaboration where you come as a leader, but they are the expert in the field, and together you can make, you can move the needle, together you can make things happen. So build that kind of trust relationship that will, that is based on their competence and your leadership and together you can really get things in motion. It’s very hard for someone who doesn’t have the strong IC technical hands-on background in a specific stack to come and lead them from a technical perspective purely with their own leadership. And that’s, in another language, that’s not a good leadership framework or management style if you just come in and guide the whole team to do what you want them to do. So that’s where, again, your soft skills get into the play where you come in and say, okay, what’s the vision here? What’s the plan what you have been going through? What are the challenges? And then, over time as you get more mature and more experienced as a leader, you’ll find a way, you’ll find a way to make it work. But again, I think you need to really get your ego outside of the room. Get and talk to these individuals. Make sure they understand you are here to support them and guide them from a leadership perspective, but they are still the expert in the fields and you count on them and give them space to experiment, give them space to own and lead and drive things. And that’s what leads to good collaboration between the leaders and the team behind.
Kovid Batra: Totally makes sense. Totally makes sense. So, um, moving on to the part where we talk about managing the teams, making them more efficient, making them more productive, what do you think, is there a framework that fits for everyone? Do you follow a framework to improve the overall engineering productivity, developer productivity in your teams?
Maher Hanafi: Honestly, this is a very kind of hard question, right? There is no pattern. There is no formula, one size fits all here for performance and for productivity. As a leader, you need to get into learning what your team is about, what the challenges they are facing, what kind of combination of skills, again, hard and soft skills you have in the team to figure out what is missing and how can you address this. But there is still like, even if this is not like a, there is no specific framework, I personally have been following a framework that helped me a lot in my journey. This is based, this is a twist of Daniel H. Pink, um, kind of autonomous team or the art of mastery, based on his book Drive. It’s by someone called, I think, John Ferguson Smart, and it’s a combination of three things. Shared understanding, which is mainly making sure that everyone in your team has the same understanding of what you are trying to do, what is the vision, and get that level of alignment, because sometimes teams cannot perform if they don’t have the same definition of something. Like if you want to build a feature and two parts of your team have this different understanding of that feature, that’s not going to lead to a highly performant outcome. So shared understanding is key and sometimes we miss this as leaders. We, we kind of delegate this to other people or other departments like product and project management say, “Okay, well, you, you, you define what is the statement and let the team work on it.” But as an engineering leader, you need to make sure your team has that same alignment.
The second thing is I list, I actually, I talked about this earlier is trust. I think trust is, again, really underrated when it comes to engineering leadership and we focus on technical and like this and that, but to build the value of trust in your team, to make sure, again, what I said earlier, talk to your team and tell them you are the expert. I’m here to help you get the best out of your expertise. And then, they should trust you also as a leader, as someone who can really help them navigate these things, not worry about the external noise and focus on what they need to deliver. And this leads to peak performance, which hopefully we’re going to get to at some point. The third part of this is competence, and this is mainly about hard skills which are, you know, very related to how efficient they can be at their, their, the stack and the technology they’re working on and all of that. So it’s more about the deep knowledge. So now defining shared understanding, trust and competence, you have overlap between these things, shared understanding and trust gives flexibility. So if you and your team members have the exact same understanding and you trust them, you can give your team the flexibility to do whatever they want. They work in their own way, the best way that works for them and own and kind of drive a higher level of ownership and use their own better judgement to get to the delivery. And flexibility works a lot to improve performance. So if you give people the flexibility they need, they can be very successful. The overlap between trust and competence provides excellence; meaning that if you trust them and they have the right skills, they will deliver the best outcome from a technology perspective. They will build the best code they can, because they trust their own frameworks and practices. Obviously you need, as a leader, you need to make sure it’s all aligned across the teams and not, it’s not based on individuals. And then last overlap is between shared understanding and competence. You get the focus. So if they have the skills and they have a clear understanding, they can be very focused on delivering exactly the right desired outcome you have for the team.
So this is the framework I use. It’s very kind of, um, very vague from, from, from distance. But when you start using it and really try to put together some specific goals and expectations to get higher on all of these, you get the center of all of these overlap, which is a very highly autonomous team that master their technology and the work they do. And again, they can have, deliver the highest impact possible. So that’s one of the frameworks, obviously there are more, but that’s one I really, that really resonated with me. Uh, I have the books, I have the TED, I mean, I watched the TED talk from Daniel H. Pink, which is really great, I recommend it to everyone.
Kovid Batra: Perfect. I think shared knowledge, competence, flexibility, trust, like when you are putting it out there as a framework, I’m sure there are some specific processes, there are some specific things that you are doing to ensure everything falls into place. So can you just give like one example that is most impactful in implementing each of these pieces? Like one, one thing that impacts a lot that you are practicing.
Maher Hanafi: Yeah. Yeah, that’s a good point. And again, that was one framework, but there is a very popular framework, PPT, right? Like people, process and technology. These are key factors influencing engineering productivity and you need to work on them. The one focused on people has two sub, sub parts, which are the individual of part of people, and then there’s the team. So you need to make sure for the individual factors, you work on skills and experience and growth development. You need to make sure people have the motivation, engagement, work life balance, and all of that. And for the team, you need to focus on communication, collaboration, team dynamics. So one good example is I worked at companies where there were very distributed teams, including contractors, you know, engineering teams. there are some in-house engineering, there are contractors engineering, the in-house are distributed, the contractors are distributed. When I joined this company, people were naming the other parties by the name of the contractor, like the company, like, “Oh, this part of the software is like owned by this and that part is owned by us, the in-house engineers.” Based in the West, as an example. And I was so confused because for me, an engineering team is one engineering team, even if it’s distributed, like these boundaries are just geo-based boundaries. They cannot be just also deep into the engineering process in work. So what I did is I made sure like all these kind of boundaries, you know, are removed, virtual boundaries are removed. Engineering team is aligned. They use the same framework. They use the same language. They use even at some point, the same technology stacks as much as possible by aligning on design patterns, uh, building SDKs, building shared components. And that kind of created more dynamics between these teams that got them to deliver higher productivity and higher impactful software. Because at the beginning, again, there was, like every team was delivering their own standards, their own patterns, even their own stacks. Like some part was written in Python. The other part was no, the other part is in Go. They were just serving each other and in a handoff process, like, “Oh, you want this? Here you go. You have this service build.” And he does this and you have an API. But as soon as you, as a manager, I needed to put resources in different teams and focus on one areas. When I had to manage that mobility of the engineers, they were going into new piece of software saying like, “I’m not familiar with the stack and I’m not.. Even for me, even if I’m familiar with the stack, I’m not familiar with the design patterns that are in this stack in this piece of software.” And for me, that was a challenge. So, one big part we forget about improving productivity is making sure from a technology perspective, the tools, the stack, the design patterns are aligned as much as possible. You introduce new systems like CI/CDs and observability to make sure things are moving along really quickly.
And then the, the second part of this is as you said earlier, it’s the process, like what methodology you have, what kind of channels to communicate, work, you know, how efficient is your workflow as a team and what kind of practices you have introduced to your teams. And these practices should be as aligned as possible across everyone, you know, including, you know, distributed teams to achieve higher performance and higher productivity in general. That was, again, that was one of the biggest learning I had when I, when my teams started scaling up and also going more distributed from a, from a geo-based location ensuring that it’s not just a handoff process between software engineers. It was more about alignment. And I think that that solution can scale with the scale of the problem as well.
Kovid Batra: Makes sense. Perfect. Perfect. I think with that, I would like to know some of your initiatives that you would have worked in the last year or must be planning a few more initiatives this year to actually impact your engineering productivity. Is there something that was challenging last year for you? You accomplished something out of it or are still working on that?
Maher Hanafi: Yeah. So, one of the biggest areas I focus on is this again, individual and team factors, the people side of things, right? Again, technology, we talked about this enough, in my opinion, process as well, but the people side of things could be tricky. And it takes a lot of time and experience to get to a place where you can have as a leader, as an engineering leader, you can have an impact on the people. So some of the biggest initiatives I work on is ensuring on the individual side of things, we have a continuous learning development of skills for everyone on the team, no matter what level they’re in, even if you are the most highly senior engineer principal and architect level, there’s still something for you to learn. There is a new area to discover in engineering and software and hands-on work, but also maybe in some other soft skills. So providing resources, time and, you know, availability to go and explore different areas that definitely could be driven by their own passion and that’s another framework I want to bring, which is something as a, going back to the first question, you know, the story of my childhood and all of that, I was passionate about video games and I wanted to work in that space because I think when people work on their passion, they can really break the limits of what’s possible. So that’s something I always bring to my work and I get to my team and I say, let’s work together on aligning on where you want to be next and how can we achieve that. And I never bring my own pattern of growth and maybe success and say, Oh, like I go to a Director of Engineering and say, “If you want to be a VP of Engineering, this is what you need to do based on what I did.” No, everyone is different. Every path and journey is different. And I, what I do is I work with them to define their own definition to get to their own definition of success. And I say, “What makes you successful? What makes you happy in working on things that you’re very excited about? What makes you more motivated and engaged?” So the other tool or framework I use is really collaborate with individual and teams to identify their own definition of success. And then I add to it some spices, I would say, from my own recipe and from my own experience as a leader to just kind of tweak it a little bit. But most of the time that’s what I focus on is like, “Tell me exactly where you see yourself. What’s your passion about?” And this could be completely like 180 degrees. It could be doing like a software engineering on the backend and then when I go into AI. And I help them to transition there, again, over time. And I think that’s the key. And I, I think, and I hope I was able to turn around a lot of people in, in, in getting into higher productivity and performance because of this, because I never go to someone and say, “You need to do this. To be successful, you need to follow this path.” I always try to listen and get their own definition of success and work with them through this and then say, “Okay, based on everything you said, based on your passion, based on your motivation and where you want to be and with my own tweaks, This is what we need to do. And I will do followups with you and we’ll work together to achieve that.” This is something, again, if you talk to anyone I worked with in the previous companies or better works today, this is something that resonates really well with people. They recognize as a working efficient way to get better over time. And when you achieve this on the individual level, obviously your teams in general will be impacted and you’ll create some sort of like leadership and ownership and people driving things. And everyone is pushing the boundaries of what you can do as an engineering team in general. And it has been very efficient. And for me as an engineering leader, that’s where I get my rewarding experience. This is where I feel I had an impact. And this is where I was able sometimes again, to turn around completely low performance into high performance.
Kovid Batra: But I think in this case, as much as I agree to what you’re saying really resonates and in fact, that could be true for any department, like any leader enabling team members in the direction where they are passionate about, would something, would be something that would energize the whole, whole team. But still, I feel that there is a lot of complication that gets added because at the end of the day, we are humans. We have changing desires, changing passions, and then a lot of things get complex. So while you implement this framework in an engineering team, what kind of challenges you have seen? Is there sometimes some kind of a shortage of a particular skill set in the team because a lot of people are more passionate about doing the back end and you have less front end engineers or maybe vice versa. So there could be a lot of such complications there. So any challenges that you’ve seen while implementing these things?
Maher Hanafi: Absolutely. I mean, you said there are some complications and challenges, but there’s a lot. I mean, there are a lot of complications and challenges when you work as an engineering leader. This is again, as I said earlier, some people call it the most difficult position to be in because you’re, you’re managing different things. Again, we talked about people, process and technology. We, we talked about hard and soft skills, but on the, on this side, when you’re trying to implement something like this, some of the examples I can bring up here to the conversation are the initiatives you have running, maybe some of the greatest initiatives you have happening in the engineering team, like, uh, at Betterworks, as an example, we are, we have been building generative AI, you know, enhanced features and bringing these great technologies, we have been kind of refactoring, revamping some of our technologies to build newer, better systems. And, but you still have the other old legacy systems. You have things are running in production that you need to maintain. You have incidents to manage and stuff like that. And sometimes you have, you know, resources, people, teams are watching other teams and other people doing other exciting stuff, and they are still like doing the old stuff. And as an, again, an engineering leader, your job is to make sure that there’s a good dynamic. There’s a good culture of, again, trust and shared understanding that these things are happening to everyone at the same time. It’s just that it takes a little bit more time in process and priorities to get there. So it’s part of that, again, earlier, when I talked about the own definition of success is to really know where everyone is eager to be doing as, again, an individual. And then, when you talk to the team in general, you need to see what you’d listen to their feedback and understand their point of view. So sometimes some teams will say, “Okay, well, we have been coding in this part of the software for like three or four years now, and nothing is moving too much.” Versus other teams where like every quarter, they have a new feature, they have great stuff, it’s being communicated and published. And it gets a lot of like credits and all of that. So you need to make sure you have the right process in your team to be able to rotate the projects, to rotate the excitement, to get people to, again, own and lead to experiment. So some of the initiatives we do are always you know, hackathons, you know, give people time to just do something completely different from what they do on a daily basis. So that will, you know, trigger the creativity of everyone, the passion again, and you can see where everyone’s mind is at and what they want to do. So again, it’s, it’s a little bit tricky. It’s not that easy. It’s not like, Oh, everyone will be doing this. And then six months later, you’ll be doing something more fun. But that’s where, again, your presence as an engineering leader is so important. Your vision is so important. You need to people to have your teams behind you in terms of vision and trust that it’s going to happen in that kind of way of rotation and mobility and everyone will be impacted.
So, absolutely, it’s one of these challenges you see, like people trying to get into more exciting projects while you have some support. One other thing you need to do as a leader is to ensure these kind of single point of failures and you cannot. afford to have one person or one team that is just expert, very deeply expert in one area. And it creates this environment where you are afraid of two things, these team or these individuals leaving and creating a gap in knowledge, or these people being stuck in that knowledge and cannot afford to do anything else. Even if they are passionate about it or they are bored of that, you know, they, they have been building this service for too long. They want to experiment something else, but you cannot let them go because you say you’re the only expert. So my job is ensure that knowledge transfer is happening, people getting into new systems, delegate a little bit and offer everyone option to get out and do something else that they’re excited about. It’s a dance, right? It’s a push and pull. You need to get into understanding how things work. and be involved a little bit deeper to be more effective as an engineering leader.
Kovid Batra: I think the core of it lies in that you have to be a good listener, not like exactly ‘listening’ listening, but being more empathetic and understanding of what everyone needs and the situation needs and try to accommodate every time because it’s going to be dynamic. It’s going to change. You just have to keep adjusting, keep tweaking, calibrating according to that. So it totally makes sense.
Maher Hanafi: And the funny part is, uh, the funny part is a lot of this I learned while playing video games. That’s gonna connect to the first question you asked. You know, when you play a video game, you’re a guild master of like 200–300 people. And you know, you go and do these raids and experiences and then you have loot to share. And you need to make decisions and everyone wants something. Yeah, you kind of build up some experience early on about people dynamics, about making sure how you make people happy and how you navigate conflicts in opinions. And sometimes when you have very senior people also, you have a clash of opinions. So how would you navigate that? How would you make sure they can work in an environment where everyone has a strong opinion about things? So yeah, a lot of this I learned early on in my journey before even I got into engineering, while playing video games and dealing with people, which is really great.
Kovid Batra: Cool. I think that’s on the people part. And I think that was really, really insightful. I think we should have some, instead of books, have the list of games that one should play early on in their life to be a manager.
Maher Hanafi: Yeah.
Kovid Batra: So moving on from people like you mentioned about technology, right? What happened in 2024 or you’re planning for 2025 in technology to make your teams even more efficient?
Maher Hanafi: Yeah, I would say a few things. Focus on technology. There are, I would say, three big pillars. One of them is really addressing poor designs, poor patterns in your software. We underestimate this again as, underthink about it as a problem that is impacting productivity and performance. When engineers are dealing with older legacy software that has poor designs, it takes time. It introduces more bugs. No matter how skilled they are, it’s challenging. So really as an engineering leader, you need to always make sure there’s time to recover, time to pay back technical debt, time to go back and redesign, refactor, and reinvent a little bit your software stack to get people to enjoy newer, more modern architecture that will lead to high performance and productivity. Things can happen fast when you have the right patterns that are more accurate, more modern today. Again, this is very, this is something I do on a, you know, frequent basis at Betterworks and before, one of my key areas of focus as an engineering leader is to help teams pay back technical debt, build better software so they can be more productive. The second thing is investing, I would say. Investing in tooling and platforming. I mean, we always forget about platform engineering as a pillar to software engineering in general, but being able to build the right continuous integration, continuous delivery system, CI/CD, you know, have proper observability in place to get all these logging and monitoring and alerts you need to be able to know and quickly debug and figure out things. It helps a lot and it makes sure, you know, it creates a good level of confidence of the team in terms of the quality of the code. And again, you can, it’s, it’s a lot of things are happening most recently, and this is where I’m going into a third kind of component that is impacting performance and productivity from a technical perspective is generative AI. And we have seen over the last two years now, the development of these co-pilots, the coding assistance. And it’s true. It’s not fully there. It’s not fully efficient so far, but it’s very effective to get a certain level of delegation to AI when it comes to like, as an example, writing tests for functions you have, for helping you optimize some of the code base, even migrate from a stack to another. So it’s a, it’s becoming a powerful tool capable of learning from your stack and your, your software learning over time as well, adapting, and even solving some problems and some real problems at some point. As a very good example at Betterworks today, we have a, you know, top-down approach to adopting generative AI. Everyone at the company is really encouraged and asked to leverage AI in their own areas of expertise and for engineering in particular, we ask everyone to use these co-pilots and coding assistants to leverage the new ideas coming up out there to experiment and really to bring use case and say, “Okay, I have been using this to achieve this thing.” I think there are very key areas again, PR, pull request work and improvement, writing tests and even infrastructure in the future seems like infrastructure could have a big area of impact when AI helps optimize infrastructure, not to build everything from scratch on behalf of people. I don’t think AI will replace software engineers, honestly, but it will make them better software engineers capable of achieving way more, be more productive and more performant. And I think that’s the goal.
Kovid Batra: Makes sense. I think when you said redesigning and taking up the new patterns, getting rid of the old ones, or if it’s about, let’s say, rewriting code pieces, generative AI is actually putting in as a fundamental piece everywhere, right? And there could be a lot of use cases. There are a lot of startups. There are a lot of tools out there. But according to you, while you were researching that which areas should be now on higher priority from an engineering standpoint and AI could really be leveraged, I think you would have first checked this tool has evolved in this area, and this could be a right fit to be used right now. Like you mentioned about co-pilots, right? It can write a better level of code and it can actually be integrated. We can try new IDs to ensure that we have better code, faster code in place. Are there any specific tools, I mean, if you’re comfortable sharing names or telling us, what could work better for other teams as well, other engineering leaders, other engineering teams outside, out there, uh, any examples or anything that you found very interesting?
Maher Hanafi: I mean, the number one tool is obviously GitHub Copilot. A lot of teams today are on GitHub anyway. So it’s very well embedded into the system and you know, a lot of plugins for all the IDE’s out there. So I think it’s the first one that comes to mind. Also now they released the free license tier that will help a lot of people get into it. So I think that’s the no brainer. But, uh, for me, I will go a little bit off a tangent here and say that one of the best ways to experiment with, E gen AI as a software engineer could be to run gen AI locally on your machines, which are things we can do today. And personally, even a, as, as an, an engineering leader not being very, very hands-on today. You know, I found out that something like a combination of Ollama which helps you run systems, I mean LLMs locally and open source models out there like, uh, the Llama 3 models or the Mistral models. You can have, you can have a local assistant to do a lot of things, including code assistant and writing code and refactoring and all of that. And add to, if you add to that some IDEs like cursor, now you can use your ID connected to your own LLM, that again, if you have the level of experience to maybe go and fine tune it over time and use, leverage Ollama to also include, do some rag and bring some more code and bring some documentations to think in very good examples on how you do tests as an example, it could be a very strong tool for more experienced engineers. And I think one of the biggest area Gen AI would have an impact is testing. I think testing, the testing pyramid has always been to fully automate, the ambition is to automate as much as possible. And I think with gen AI, there will be more use cases to just do that. If you leverage generative AI to write tests, I think you will have a bigger, better suite of tools to ensure that your quality of code is meeting a certain level to test for edge cases you didn’t think about when you were writing code. So I think testing is one area. The other area would be in general research, honestly, in learning as a software engineer, if you have a co-pilot or just any LLM or chat based LLM, like chatGPT or Gemini or Claude, you can go and really, you know, learn about things faster. Yes, it does a lot of things for you. Like, as an example, you can copy paste a function, say, “Hey, can you optimize this?” The key if you’re leveraging generative AI is learning. It’s not to delegate. I mean, some people might think, “Oh, I don’t have to worry about this. I’m going to write random code, but then the, uh, gen AI will optimize it for me.” The key is for you to learn from that optimization that was offered to you. And we should not forget, you know, LLMs are not perfect and you can think about them as another software engineer, maybe more experienced for sure, but an engineer who can make mistakes. So it’s your part to be really curious and critical about the outcome you get from GenAI to make sure you’re at the same time leveraging the tool to learn, to grow, and to have a bigger impact and be more productive.
Kovid Batra: Yeah, I think these are some of the hard truths about AI, uh, code assistance, but lately I’ve been following a few people on LinkedIn, and I’ve seen different opinions on how Copilot has actually helped in improving the code writing speed or in general, the quality. There is a mixed opinion. And in such situations, I think any engineering org which is implementing such technology would want to have clarity on whether it is working out for them or not, and it’s completely possible that it works out for some companies and it doesn’t for some. In your case, do you like measure specific things when you, let’s say, implement the technology or you implement a new process just to, like, improve productivity, is there something that you specifically look at while implementing those at the beginning and the end to ensure, like, okay, if this is working out or not?
Maher Hanafi: Yeah, I mean, some things are measurable. Some things are not measurable, honestly, and this is known, you know, the challenge is to measure the immeasurable to find out where this technology is having impact without having tangible metrics to measure. And you need to use proxies based on that. You need to collect feedback. You need to get some sort of an assessment of how you feel about your own productivity as an engineer using these tools. So we do that every once in a while. Again, we have a very specific internal strategy and vision that is driven by, I mean, that is focused on using and leveraging generative AI in every area of the business, and one of them is software engineering. And when we started, one of the very good use cases, again, was QA and writing tests. And we have been measuring how much time it takes, I would say, a software development in tests to write the suite of tests for a new piece of code. We try to compare both, you know, ways the old ways, which is mainly kind of manual, like let’s look at this, let’s write all the tests that are needed or define the test suite for these, and then the other way is QA, you share the QA, the concept, the requirements, the acceptance criteria, and then you expect it to generate for you the test. And we have noticed that the time that takes an engineer in a software development engineering test to get to the desired outcome is way more significant. I don’t have exact percentages or numbers, but it’s like it takes 20 percent time versus, you know, a hundred percent to just achieve the whole test suite. So for, you know, this area of like bringing generative AI, it’s good, but again, we should not forget that these tests, you know, have to be reviewed. The human should be in the loop. I don’t believe in a lot of things to be fully automated and you don’t have to worry about, and you don’t have to look back. But I also, on another end, I really believe that Gen AI will become table stakes in software engineering. The same way we had these great IDs developing over time, the same way we had autocomplete for code, the same way we had process and tools to improve our quality of code, the same way we had patterns and, you know, things, I think Gen AI will become that thing that we all use, we all have, it’s common knowledge and it’s going to be a shift in the way we work as software engineers. You know, we used to use a lot of Stack Overflow and go and search and do this and do that. All that will be replaced now in your own environment, in the work and the flow of work and you will have all the answers you need. I don’t think it will take over software engineering 100 percent and like you don’t have to write anything and you hear, and you see this in LinkedIn, as you said, you hear like, oh, this was developed. I think these are, as of today, these are naive, you know, thinking about software engineering. You know, you can build a proof of concept, you can build some basic, one single feature aspects, but as you get to build enterprise, you know, distributed systems, this doesn’t scale to that level. But the technology is evolving and GenAI is doing its best to get there, and we’re here for it. We’re here to support that, and we’re here to learn it, and use it. But again, we all go back to the same saying of like a software engineer who’s leveraging generative AI will be more productive and efficient than a software engineer who doesn’t.
Kovid Batra: Makes sense. All right. I think with that, we come to the end of this episode. I could continue talking to you. It’s super, super exciting and insightful to hear all the things that you have been doing. I think you are a really accomplished engineering leader. It is very evident from what you’re saying, what you’re doing at the organization, at your organization. It is very difficult to be in this overwhelming position. It, it, it looks like that it is very overwhelming. So any piece of advice to all the other engineering leaders who are listening to you? How to keep that sanity in place while managing this whole chaos?
Maher Hanafi: I think it’s a matter of, again, going in circles here, but it’s, it’s a passion, right? I think you need to have the level of passion to be able to navigate this role. And the passion is what keeps you pushing the boundaries in making things that are complex and hard and challenging look easy and look fun and enjoyable, right? Some parts of my work are hard and tough, but I honestly enjoy them and I go through them with a positive attitude, it’s like, “This is a tough conversation I need to have. This is it. You know, I’m going to bring my principal engineers. We’re going to talk about something. And I know everyone will have an opinion, but you know what? We need to leave this meeting with a decision.” And, you know, you need to have the passion to be able to navigate these complexities. Being someone who is very driven about solving problems, navigating people dynamics, passion about technology, obviously, and have a good mindset of getting, you know, getting to the finish line. So we, you have been asking about a lot of frameworks and other frameworks, which again, very popular one is get things done. GTD. As an engineering leader, a VP for Engineering, you need to get things done. That’s your job. So you need to be passionate about that. Get to the finish line. So it’s a lot of things here and there. I don’t recommend engineering leadership in general. For people who are very passionate about just pure technical things, people who are very passionate about coding, it’s, it’s going to be very hard for them to detach from coding and technology aspect and get into navigating these things. So when you get to this level, you focus about different things from just the perfect code that you’ll ever write, and it’s more about the perfect outcome you can get out of the resources you have and have an impact. I use this word a lot. I think engineering leaders are all about impact and all about getting the best resources or the best outcomes from the resources they have and even minimize our resources, obviously, time and money in this case. So it’s not easy. But if you have the passion, you can make things happen and you can turn these complex things into fun challenges to have and solve them and really get that rewarding experience at the end where you go, “You know what? I came here, there was a big challenge, there was a big problem, I helped the team solve it, let’s move on to the next big thing.” And I think that’s my advice to people who are looking to become engineering leaders.
Kovid Batra: Perfect. On point. All right, Maher. Thank you. Thank you so much for your time. And we would love to have you again on the episode for sure, sometime again, and talk more in depth, what you’re doing, how you’re leading the teams.
Maher Hanafi: Thank you again. Thank you so much. I really appreciate it. Thank you for having me on, on your podcast.
DORA metrics—like lead time for changes and mean time to restore—are essential, but Maher emphasizes pairing them with leading indicators such as PR review turnaround, deployment frequency, and developer satisfaction scores.
By building clarity through OKRs, encouraging team-level ownership, and using async rituals (like pre-recorded demos and written standups) instead of meetings to track progress.
Over-indexing on individual outputs (like LOC or ticket count). Maher warns this breaks team trust and ignores bottlenecks in flow—like overloaded reviewers or unclear priorities.
In this episode of the groCTO Podcast, host Kovid Batra interviews David Archer, the Director of Software Engineering at Imagine Learning, with over 12 years of experience in engineering and leadership, including a tenure at Amazon.
The discussion centers on successfully integrating acquired teams, a critical issue following company mergers and acquisitions. David shares his approach to onboarding new team members, implementing a buddy system, and fostering a growth mindset and no-blame culture to mitigate high attrition rates. He further discusses the importance of having clear documentation, pairing sessions, and promoting collaboration across international teams. Additionally, David touches on his personal interests, emphasizing the impact of his time in Japan and his love for Formula 1 and rugby. The episode provides insights into the challenges and strategies for creating stable and cohesive engineering teams in a dynamic corporate landscape.
Kovid Batra: Hi, everyone. This is Kovid, back with another episode of groCTO podcast. And today with us, we have a very special guest. He has 12 plus years of engineering and leadership experience. He has been an ex-Software Development Manager for Amazon and currently working as Director of Engineering for Imagine Learning. Welcome to the show, David. Great to have you here.
David Archer: Thanks very much. Thanks for the introduction.
Kovid Batra: All right. Um, so there is a ritual, uh, whosoever comes to our podcast, before we get down to the main section. So for the audience, the main section, uh, today’s topic of discussion is how to integrate the acquired teams successfully, uh, which has been a burning topic in the last four years because there have been a lot of acquisitions. There have been a lot of mergers. But before we move there, uh, David, we would love to know something about you, uh, your hobbies, something from your childhood, from your teenage or your, from personal life, which LinkedIn doesn’t tell and you would like to share with us.
David Archer: Sure. Um, so in terms of my personal life, the things that I’ve enjoyed the most, um, I always used to love video games as a child. And so, one of the things that I am very proud of is that I went to go and live in Japan for university and, and that was, um, a genuinely life-changing experience. Um, and I absolutely loved my time there. And I think it’s, it’s had a bit of an effect on my time, uh, since then. But with that, um, I’m very much a fan of formula one and rugby. And so, I’ve been very happy in the last, in the post-COVID-19 years, um, of spending a lot of time over in Silverstone and Murrayfield to go and see some of those things. So, um, that’s something that most people don’t know about me, but I actually quite like my sports of all things. So, yeah.
Kovid Batra: Great. Thanks for that little, uh, cute intro and, uh, with that, I think, uh, let’s get going with the main section. Uh, so integrating, uh, your acquired team successfully has been a challenge with a lot of, uh, engineering leaders, engineering managers with whom I have talked. And, uh, you come with an immense experience, like you have had been, uh, engineering manager for OVO and then for, uh, Amazon. I mean, you have been leading teams at large organizations and then moving into Imagine Learning. So before we touch on the topic of how you absorbed such teams successfully, I would love to know, how does this transition look like? Like Amazon is a giant, right? And then you’re moving to Imagine Learning. Of course, that is also a very big company. But there is definitely a shift there. So what made you move? How was this transition? Maybe some goods or bads, if you can share without getting your job impacted.
David Archer: Yeah, no problem. Um, so once upon a time, um, you’re correct in terms of that I’ve got, you know, over 12 years experience in the industry. Um, but before that, I was a teacher. So for me, education is extremely important and I still think it’s one of the most rewarding things that as a human you can be a part of. Helping to bring the next generation, or in terms of their education, give them better, uh, capabilities and potential for the future. Um, and so when somebody approached me with the position here at Imagine Learning, um, I had to jump at the chance. It sounded extremely exciting and, um, I was correct. It was extremely exciting. There’s definitely been a lot of movement and, and I’m sure we’ll touch on that in a little while, but there is definitely a, a, quite a major cultural shift. Um, and then obviously there is the fact that Amazon being a US-centric company with a UK arm, which I was a part of, um, Imagine Learning is very similar. Um, it’s a US-centric company with a US-centric educational stance. Um, and then, yeah, me being part of the UK arm of the company means that there are some cultural challenges that Amazon has already worked through that Imagine Learning still needed to work through. Um, and so part of that challenge is, you know, sort of educating up the chain, if you like, um, on the cultural differences between the two. So, um, definitely some, some big changes. It’s less easy to sort of move sideways as you can in companies like Amazon, um, where you can transition from one team to another. Um, here, it’s a little bit more, um, put together. There’s, there’s, there’s only one or two teams here that you could potentially work for. Um, but that’s not to say that the opportunities aren’t there. And again, we’ll touch on that in a little bit, I’m sure.
Kovid Batra: Perfect. Perfect. All right. So one, one question I think, uh, all the audience would love to know, like, in a company like Amazon, what is it like to get there? Because it takes almost eight to 10 years if you’re really good at something in Amazon, spend that time and then you move into that profile of a Software Development Manager, right? So how, how was that experience for you? And what do you think it, it requires, uh, in an Engineering Manager at Amazon to be there?
David Archer: That’s a difficult question to answer because it changes upon the person. Um, I jumped straight in as a Software Development Manager. And in terms of what they’re looking for, anybody that has looked into the company will be aware of their leadership principles. And being able to display their leadership principles through previous experiences, that’s the thing that will get you in. So if you naturally have that capability to always put the customer first, to ensure that you are data-driven, to ensure that you have, they call it a bias for action, but that you move quickly is kind of what it comes down to. Um, and that you earn trust in a meaningful way. Those are some of the things that I think most managers would be looking for, and when interviewing, of course, there is a technical aspect to this. You need to be able to talk the talk, and, um, I think if you are not able to be able to reel off the information in an intrinsic manner, as in you’ve internalized how the technology works, that will get picked up. Of course it will. You can’t prepare for it like you can an exam. There is an element of this that requires experience. That being said, there are definitely some areas that people can prepare for. Um, and those are primarily in the area of ensuring that you get the experiences that meet the leadership principles that will push you into that position. In order to succeed, it requires a lot of real work. Um, I’m not going to pretend that it’s easy to work at a company like Amazon. They are well known for, um, ensuring that the staff that they have are the best and that they’re working with the best. And you have to, as a manager, ensure that the team that you’re building up can fulfill what you require them to do. If you’re not able to do that, if you’re taking people on because they seem like they might be a good fit for now, you will in the medium to long-term find that that is detrimental to you as a manager, as well as your team and its capabilities, and you need to be able to then resolve that potential problem by making some difficult decisions and having some difficult conversations with individuals, because at the end of the day, you as a manager are measured on what your team output, not what you as an individual output. And that’s a real shift in thinking from being a, even a Technical Lead to being an Engineering Manager.
Kovid Batra: That’s for sure there. One thing, uh, that you feel, uh, stands out in you, uh, that has put you in this position where you are an SDM at Amazon and then you transitioned to a leadership position now, which is Director of Engineering at Imagine Learning. So what is that, uh, one or two traits of yourself that you might have reflected upon that have made you move here, grow in the career?
David Archer: I think you have to be very flexible in your thinking. You have to have a manner of thinking that enables for a much wider scope and you have to be able to let go of an individual product. If your thinking is really focused on one team and one product and it stays in that single first party of what you’re concentrating on that moment in time, then it really limits your ability to look a little bit further beyond the scope and start to move into that strategic thinking. That’s where you start moving from a Software Development Manager into a more senior position is with that strategic thinking mindset where you’re thinking beyond the three months and beyond the single product and you’re starting to move into the half-yearly, full-yearly thinking is a minimum. And you start thinking about how you can bring your team along for a strategic vision as opposed to a tactical goal.
Kovid Batra: Got it. Perfect. All right. So with that, moving to Imagine Learning, uh, and your experience here in the last, uh, one, one and a half years, a little more than that, actually, uh, you, you have, uh, gone through the phase of your self-learning and then getting teams onboarded that were from the acquired product companies and that experience when you started sharing with me on our last, last call, I found that very interesting. So I think we can start off with that point here. Uh, like how this journey of, uh, rearranging teams, bringing different teams together started happening for you. What were the challenges? What was your roadmap in your head and your team? How will you align them? How will you make the right impact in the fastest timeframe possible? So how things shaped up around that.
David Archer: Sure. Initially, um, the biggest challenge I had was that there was a very significant knowledge drain before I had started. Um, so in the year before I came on board and it was in the first year post-acquisition, the attrition rate for the digital part of the company was somewhere in the region of 50%. Um, so people were leaving at a very fast pace. Um, I had to find a way to plug that end quickly because we couldn’t continue to have such a large knowledge drain. Um now the way that I did that was I, I believe in, in the engineers that I have in front of me. They wouldn’t be in the position that they’re in if they didn’t have a significant amount of capability. But I also wanted to ensure that they had and acquired a growth mindset. Um, and that was something that I think up until that point they were more interested in just getting work done as opposed to wanting to grow into a, a sort of more senior position or a position with more responsibility and a bigger challenge. And so I ensured that I mixed the teams together. We had, you know, front enders and back enders in separate teams initially. And so I joined them together to make sure that they held responsibility for a piece of work from beginning to end, um, which gave them autonomy on the work that they were doing. I ensured that I earner trust with that team as well. And most importantly, I put in a ‘no-blame culture’, um, because my expectation is that everybody’s always acting with the best of intentions and that usually when something is going wrong, there is a mechanism that is missing that would have resolved the issue.
Kovid Batra: But, uh, sorry to interrupt you here. Um, do you think, uh, the reasons for attrition were aligned with these factors in the team where people didn’t have autonomy, uh, there was a blame game happening? Were these the reasons or, uh, the reasons were different? I mean, if you’re comfortable sharing, cool, but otherwise, like we can just move on.
David Archer: No, yeah, I think that in reality there, there was an element of that there, there was a, um, a somewhat, not toxic necessarily culture, but definitely a culture of, um, moving fast just to get things done as opposed to trying to work in the correct manner. And that means that people then did feel blamed. They felt pressured. They felt that they had no autonomy. Every decision was made for them. And so, uh, with more senior staff, especially, you know, looking at an MNA situation where that didn’t change, they didn’t see a future in their career there because they didn’t know where they could possibly move forward into because they had no decision-making or autonomy capability themselves.
Kovid Batra: Makes sense. Got it. Yeah, please go on. Yeah.
David Archer: Sorry, yes. So, um, we’re putting these things in place, giving everybody a growth mindset mentality and ensuring that, um, you know, there was a no-blame culture. There were some changes in personnel as well. Um, I identified a couple of individuals that were detrimental to the team and those sort of things are quite difficult, you know, moving people on who, um, they’re trying their best and I don’t deny that they are, but their way of working is, is detrimental to a team. But with those changes, um, we then move from a 50% regressive attrition to a 5% regressive attrition over the course of 23 and 24, which is a very, very significant change in, um, in attrition. And, uh, we also, at that point in time, were able to start implementing new methodologies of bringing in talent from, from below. So we started partnering with Glasgow University to bring in an internship program. We also took on some of their graduates to ensure that we had, um, for once with a better phrase, new blood in the team to ensure that we’re bringing new ideas in. Um, and then we prepared people through the training programs that they should need.
Kovid Batra: I’m curious about one thing, uh, saying that stopping this culture of blame game, uh, is definitely, uh, good to hear, but what exactly did you do in practice on a daily level or on a weekly level or on every sprint level that impacted and changed this mindset? What, what were the things that you inculcated in the culture?
David Archer: So initially, um, and some people think that this might be a trite point, but, um, I actually put out the policy in front of people. I wrote it down and put it in front of people and gave them a document review session to say, “This is a no-blame culture, and this is what I mean by that.” So that people understood what my meaning was from that. Following that, um, I then did have a conversation with some of the parts of, you know, some people in other parts of the company to say, “Please, reroute your conversations through me. Don’t go directly to engineers. I want to be that, that point of contact going forward so that I can ensure that communication is felt in the right manner and the right capacity.” And then, um, the, the other thing is that we started bringing in things like, um, postmortems or incident response management, um, sessions that, that where we, I was very forceful on ensuring that no names were put into these documents because until that point, people did put other people’s names in, um, and wanted to make sure that it was noted that it was so and so’s fault. Um, and I had to step on that very, very strongly. I was like, this could have been anyone’s fault. It’s just that they happen to be at that mine of code at that point in time. Um, and made that decision, which they did with a good intention. Um, so I had to really step in with the team and every single post mortem, every major decision in that, that area, every sprint where we went through what the team had completed in terms of work and made sure we did pick out individuals in terms of particularly good work that they did, but then stepped very strongly on any hint of trying to blame someone for a problem that had happened and made it very clear to them again that this could have happened to anyone and we need to work together to ensure it can’t happen to anyone ever again.
Kovid Batra: Makes sense. So when, when this, uh, impact started happening, uh, did you see, uh, people from the previous, uh, developers, like who were already the part of Imagine Learning, those were getting retained or, uh, the ones who joined after acquisition from the other company, those developers were also getting retained? How, how did it impact the two groups and how did they like, gel up later on?
David Archer: Both actually. Yeah. So the, the staff who were already here, um, effectively the, the, the drain stopped and there weren’t people leaving anymore that had had, you know, some level of tenure longer than six months, um, at all from that point forward, and new staff that were joining, they were getting integrated with these new teams. I implemented a buddy system so that every new engineer that came in would have somebody that they could work alongside for the first six months and show that they had some, somebody to contact for the whole time that they were, um, getting used to the company. And, uh, I frequently say that as you join a company like this, you are drinking from a fire hose for the first couple of months. There’s a lot of information that comes your way. Um, and so having a buddy there helped there. Um, I added software engineering managers to the team to ensure that there were people who specifically looked after the team, continue to ensure there was a growth mindset to continue to implement the plans that I had, um, to make these teams more stable. Um, and that took a while to find the right people, I will say that. Um, there was also a challenge with integrating the teams from our vendors in, um, international, uh, countries. So we worked with some teams in India and some teams in the Ukraine. Um, and with integrating people from those teams, there was some level of separation, and I think one of the major things we started doing then was getting the people to meet in a more personal manner, bringing them across to our team to actually meet each other face-to-face, um, and realize that these are very talented individuals, just like we are. They’re, they’re no different just because they, you know, live a five and a half hour time zone away and doesn’t mean that they’re any less capable. Um, they just have a different way of working and we can absolutely work with these very talented people. And bringing them into the teams via a buddy, ensuring that they have someone to work with, making sure that the no-blame culture continued, even into our contractors, it took a while, don’t get me wrong. And there were definitely some missteps, um, but it was vital to ensuring that there was team cohesion all the way across.
Kovid Batra: Definitely. And, uh, I’ve also experienced this, uh, when talking to other, uh, engineering leaders that when teams come in, usually it is hard to find space for them to do that impactful work, right? So you, you need to give those people that space in general in the team, which you did. But also at the same time, the kind of work they are picking up, that also becomes a challenge sometimes. So was that a case in your scenario as well? And did you like find a way out there?
David Archer: It was the case here. Um, there definitely was a case of the, the work was predefined, if you like, to some extent by the, the most senior personnel. And so one of the things that we ensured that we did, uh, I worked very closely with our product team to ensure that this happened is that we brought the engineers in a lot sooner. We ensured that this wasn’t just the most senior member of the team, but instead that we worked with different personnel and de-siloing that information from one person to another was extremely important because there were silos of information within our teams. And I made it very clear that if there’s an incident and somebody needs some help, and there’s only one person on the team, um, that is capable of actually working, then, um, we’re going to find ourselves in, in a real problem. Um, and I think people understood that intrinsically because of the knowledge loss that had happened before I started, or just as I was coming on board, um, because they knew that there were people who, you know, knew this part of the code base or this database or how this part of infrastructure worked, and suddenly we didn’t have anybody that had that knowledge. So we now needed to reacquire it. And so, I ensured that the, you know, this comes from an Amazon background, so anybody that, that has worked at this company will know what I’m talking about here, but documentation is key. Ensuring document reviews was extremely important. Um, those are the kind of things, ensuring that we could pass on information from one person to another from one team to another in the most scalable fashion, it does slow you down in delivery, but it speeds you up in the longer term because it enables more people to do a wider range of work without needing to rely on that one person that knows everything.
Kovid Batra: Sure, definitely. I think documentation has been like always on the top of, uh, the priority list itself now whomsoever I’m talking to, because once there are downturns and you face such problems, you realize the importance of it. In the early phase, you are just running, building, not focusing on that piece, but later on, it becomes a matter of priority for sure. And I can totally relate to it. Um, so talking about these people, uh, who have joined in and you’re trying to integrate, uh, they definitely need some level of cultural alignment also, like they are coming from a different background, coming into a new company. Along with that, there might be requirements, you mentioned like skill development, right? So were there any skill development plans that worked out, that worked out here that you implemented? Anything from that end you want to share?
David Archer: Yeah, absolutely. So with joining together our teams of frontend and backend developers, um, that’s obviously going to cause some issues. So some developers are not going to be quite as excited about working in a different area. Um, but I think with knowing that the siloing of information was there and that we had to resolve that as an issue and then ensuring that people who are being brought on via, you know, vendors from international countries and things like that, um, what we started to do was to ensure that we put in, um, pairing sessions with all of our developers. Up until that point, they kind of worked on their own and so, um, I find that working one-to-one with another individual tends to be the fastest way to learn how the things work, work in the same way as, um, a child learns their language from their parents far faster than they ever would from watching TV. Um, although sometimes I do wonder about that myself with my daughter singing baby shark to me 16 times and I don’t think I’ve ever sung that. So let’s see where that goes. Um, but having that one-to-one, um, relationship with the person means that we’re able to ask questions, we’re able to gain that knowledge very quickly. Having the documentation backing that up means that you’ve got a frame of reference to keep going to as well. And then if you keep doing that quite frequently and add in some of the more abstract knowledge sharing sessions, I’m thinking like, um, a ‘launch and learn’ type sessions or lightning talks, as well as having a, a base of, sort of a knowledge base that people can learn from. So, obvious examples of things like Pluralsight or O’Reilly’s library. Um, But we also have our own internal documentation as well where we give people tutorials, we walk people through things, we added in a code review session, we added in a code of the sprint and a session as well for our um, sprint reviews that went out to the whole team and to the rest of the company where we showed that we’re optimizing where we can. And all these things, they didn’t just enable the team to, to become full stack and I will say all of our developers now are full stack. I’d be very surprised if there are any developers I’m working with that are not able to make a switch. But it also built trust with the rest of the company as well and that’s the thing with being a company that has been acquired is that we need to, um, very quickly and very deliberately shout about how well we’re doing as a company so that they can look at what we’re doing and use us, as has frequently been the case recently actually as a best practice, a company that’s doing things well and doing things meaningfully and has that growth mindset. And we start then to have conversations with the wider company, which enables things like a tiger team type session that enables us to widen our scope and have more same company. It’s kind of a spiral at that point in time because you start to increase your scope and with doing that, it means that your team can grow because you know, that they know that thing, that they can trust us to do things effectively. And it also gives, going back to what I said at the beginning, and people more autonomy, then more decision-making capabilities they need to get further out into a company.
Kovid Batra: And in such situations, the opinions that they’re bringing in are more customer-centric. They have more understanding of the business. All those things ultimately add up to a lot of intrinsic incentivization, I would say. That if I’m being heard in the team, being a developer, I feel good about it, right? And all of this is like connected there. So I, it totally makes sense. And I think that’s a very good hack to bringing new, uh, people, new teams into the same, uh, journey where you are already continuing. So, great. I think, uh, with that, we have, uh, come to, uh, the end of this discussion. And in the interest of time, we’ll have to pause here. Uh, really loved talking to you, would love to know more such experiences from you, but it will be in the, maybe in the next episodes. So, David, once again, thanks a lot for your time. Thanks for sharing your experiences. It was great to have you here.
David Archer: Thank you so much and I really appreciate, uh, the time that you’ve taken with me. I hope that this proves useful to at least one person and they can gain something from this. So, thank you.
Kovid Batra: I’m sure it will be. Thank you. Thank you so much. Have a great day ahead.
David Archer: Thank you. Cheers now!
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