In this episode of the Data Engineering Central Podcast, I sit down with Yordan Ivanov, Head of Data Engineering at a growing fintech company, to talk through what it actually looks like to build and run real data platforms in production.
Yordan’s story starts like many of mine, early programming, gaming, PHP, Linux servers—but what makes this conversation interesting is how he evolved from a generalist software engineer into a data engineering leader without even realizing it at first.
We spend a lot of time digging into what actually matters in modern data engineering, and it’s not the tools.
We talk about:
Why the industry went too far into complexity and is now swinging back toward simplicity
The reality of running a data platform at scale (and why most teams waste time maintaining tools instead of delivering value)
How to think about migrations the right way without breaking everything
The difference between junior, mid, and senior engineers—and why ambiguity tolerance and impact matter more than coding ability
Why “perfect” engineering is a trap and how to actually ship work that matters
We also get into AI, and Yordan has one of the more grounded takes you’ll hear right now. Most companies aren’t even close to ready for AI, and the idea that it’s replacing engineers anytime soon misses the bigger problem: messy data, unclear metrics, and weak foundations.
Check out Yordan’s Substack below!
We also talk about:
How AI is actually used on real teams today (not Twitter hype)
Why juniors with AI can be risky without strong processes
How to think about code reviews, testing, and slowing down when it matters
On top of that, we dig into content creation, Substack, and what it takes to stand out in a world full of generic AI-generated content. Yordan’s approach is simple: write from real experience or don’t write at all.
This is one of those conversations that cuts through a lot of noise and gets back to fundamentals, how to think, how to build, and how to grow as an engineer in a rapidly changing space.
