In this conversational video, Ryan Kachovan (Head of Customer Strategy) and Nick Arcolano (Head of Research at Jellyfish) introduce and dissect the findings of Jellyfish’s fourth State of Engineering Management Report. The report draws on platform data covering roughly 13,000 engineering teams (about 80,000 engineers) and surveys of several hundred engineering leaders. The discussion highlights 2022 as a pivotal year of transition, marked by widespread hiring freezes (44% of respondents) and massive reprioritization of work. Participants describe the rapid, often disorienting shift—termed “thrashing” or “whiplash”—as teams moved from growth‑focused agendas to efficiency‑driven imperatives. This sudden change created inertia, making it hard for engineers to abandon existing projects. Leaders grappled with balancing efficiency gains against maintaining management styles that preserve retention and engineer satisfaction. An emerging practice identified is the allocation of roughly 20% of effort to new priorities, allowing teams to pivot without over‑hauling existing workflows. The conversation blends data‑driven insights with field observations, illustrating how engineering leadership is adapting to a turbulent environment.
Chapters
Quick introductions. Again, I'm Ryan Kachovan, the head of customer strategy. My background is a 50/50 split between consulting and operating roles, all things focused on engineering, R&D, and all things surrounding engineering and R&D. Particularly around translating that to the business, driving R&D efficiencies and things of that nature. I'd like to introduce my esteemed colleague here, Nick, if you want to give a quick overview of your background. Sure.
Hi, Nick Arcolano. Glad to see everyone here. I'm the head of research here at Jellyfish. I've been here, geez, almost 5 years. Wow. And so our research department, we're, we're the innovation, data science, and analytics function here at Jellyfish. And so, you know, we're the folks that compile and look at a lot of this data from the, you know, analytics side about what we're seeing in our platform. Yeah, so let's, let's get into it. So we're here to talk about our State of Engineering Management Report. So this is something we've been compiling This is our 4th one, I think. And so, you know what, hopefully a lot of you have looked at the report itself. If not, you should definitely check it out. But we go to the next slide. So, you know, there's, there's 2 main parts to this. So we compile a large set of the data that we see here in our jellyfish platform. So, you know, this is our largest one yet. We've compiled a subset of our data that corresponds to about 13,000 engineering teams, and you know the work that they do in, you know, Jira and Git, the performance metrics, and also the allocations stuff, you know, seeing like, you know, from a strategic perspective, you know, how do their respective businesses think about their time? That corresponds to about 80,000 engineers total, you know, whose work we're analyzing here. And then there's another part which is these surveys that we do. So several hundred engineering leaders who we talk to to get the context to really understand what's going on in their teams and their businesses that helps explain what we're seeing here in the data. And this year, this is wild, Ryan. This year, we saw— it's kind of a year unlike any other, which is probably not surprising to folks. Who've been out there, you know, like us who are out there seeing it. You know, if you go to the next slide, you know, it was 2022, everything that we see in the data, what we were hearing from folks, it was a year of transition, right? It started off one way, ended up another way. You know, 2021 and 2023 are two very different places. And we see in the data that 2022 was the year where that transition happened. You know, we saw in the survey results folks talking about, you know, 44% talked about hiring freezes. You know, almost everyone had some reprioritization of work. You know, I'd love to know, Ryan, what you are seeing in the field that mirrors like what we see in the data and what we've certainly been feeling, you know, from the folks in this industry.
Yeah, Nick, I think before I even get into that, I think we were chatting a bit about this report, some of the findings, and it was interesting to hear that year over year we started to see little changes, right? Some maturation, maybe a slight shift here or there. But the seismic shift that we started to see in 2022 and just like the demonstrative change in the data itself and the data was starting to tell us, I think was surprising. And probably from a data science perspective, I'm like, yeah, it just looks different. Like the velocity of change is something that we've seen like 3 years combined into one.
Yeah, it was reassuring to see how different things looked given how different things felt.
Yeah. And I think that amplified when we had the chance to talk with customers too, and survey respondents and actually dig into that to validate like, hey, what are some of the background we're seeing here? And we're seeing a lot of different challenges, right? I think one of the biggest themes that popped out was the idea that a lot of things shifted quickly, right? And I think what a lot of teams start to struggle with was trying to hold off the inertia that was coming through with historical processes and historical focus. So as you shift from priority A to priority B, or you change things suddenly, it's really hard to get engineers to want to stop working on that thing that they were super passionate about, dug into, or even just find clean breaks to pick it up later at a future point. A second thing that we thought came out a lot was actually this idea of thrashing. Whiplash might be a better word for it. And it's really just the idea of like the pace of the shift, right? And we saw that in the data, but it started coming out in conversations too, where people saw this sudden change going from, hey, we're gonna continue to grow at all costs, you know, the follow-up of essentially 12-year bull market. And now people are saying, whoa, we have to change what we're doing. We have to really quickly pivot and work on efficiencies and things of that nature. And I think that kind of goes along with the inertia element. But kind of maybe explains it is like how fast it all had to change, or at least the fast it did change. The other thing that really stood out, and maybe we can riff on this for a minute, but like justifying some of the key priorities and being able to effectively manage their teams in a way that balances this efficiency but without having to change their management styles. Because there's large impact on things like retention and engineer satisfaction that drive efficiency and drive effectiveness is maybe a better word for it. So we started to see like a shift in how to do that and different techniques being explored. And people are starting to learn that like, you know, having a conversation at like a higher level around like, hey, we should probably put about 20% of our effort on this feature as opposed to worrying specifically about where everybody's spending their time seemed to be the most effective approach people started to land on. I don't know if anything else stands out for you from a data science perspective there, from your experience just working day in, day out with engineering.
Yeah, I mean, the, the, the thrashing stuff and the changes and the transient nature, you know, the idea that like there's sort of a turbulent period, you know, you see this all the time and sort of the way that, that growth and change works is there's a period of turbulence and then a new normal. And we've all felt it where, you know, you identify that things you have to change well before you're able to figure out what the change ought to be, right? And sometimes there's a few false starts. And so I think a lot of what we're seeing in the data is evidence of people trying those changes, trying to digest, figure out what the changes need to be.
