Data Engineering Central Podcast

Victor Moreno went from failing out of a top CS program to becoming a senior engineer at AWS, and his story says a lot about what actually matters in software engineering today.
In this conversation, we go deep into the reality behind the AI hype, what makes engineers valuable (it’s not writing more code), and why the future of the field looks very different from what most people think.
We talk about the shift from coding to system thinking, why fundamentals matter more in the age of AI, and how junior engineers will need to evolve as tools like Claude and ChatGPT take over the “grunt work.”
Victor also shares hard-earned lessons from teaching, startups, consulting, and building systems at AWS, along with practical advice for engineers looking to stand out in a crowded, uncertain job market.
This is not a hype conversation. It’s a real look at where things are going.
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🔑 What We Cover
* Why AI is making fundamentals more important, not less
* The biggest mistake engineers make is chasing promotions
* How to actually become a high-impact engineer
* Why does doing more Jira tickets not matter
* What’s broken about today’s interview process
* The future of junior engineers in an AI world
* Tactical vs strategic engineering (and why it matters)
* Why most AI-generated code is still “low quality.”
* How to think about career growth in a weird job market
💡 Key Takeaway
The best engineers aren’t the ones writing the most code—they’re the ones who understand systems, think long-term, and can drive decisions.
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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.
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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.
Data Engineering Central is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

In this episode of the Data Engineering Central Podcast, I sit down with Jacob Matson, Developer Advocate at MotherDuck, to unpack one of the most interesting shifts happening in data engineering right now.
Jacob didn’t start in tech the way most people expect. He began in accounting, working with Excel and financial systems, before slowly realizing that the real problem he loved solving wasn’t finance, it was data pipelines. That path eventually led him deep into SQL Server, data warehousing, and ultimately to DuckDB, a tool that fundamentally changed how he thought about processing data.
* What we get into is bigger than just tools, though.
We talk about why DuckDB exploded in popularity, what it gets right that traditional databases and even modern cloud warehouses struggle with, and why the industry may be swinging back toward simplicity after years of over-engineered “modern data stacks.”
There’s a really interesting thread here around how engineers accidentally created too much complexity, and now tools like DuckDB are winning by removing it.
We also go deep on the evolution of the data stack itself. From SQL Server’s “everything in one box” model, to the unbundled chaos of the modern stack, and now back toward a more unified, simpler approach. Jacob shares how MotherDuck is thinking about that shift and where things are headed next.
* One of the more important parts of this conversation is around AI.
There’s a strong argument here that AI doesn’t kill data engineering; it massively expands it. Instead of fewer queries being written, we may be heading toward a world where AI agents generate orders of magnitude more queries than humans ever could. That flips a lot of assumptions on their head, especially around things like data modeling, which suddenly becomes more important, not less.
We also talk about:
* Why most Spark workloads are overkill
* When single-node tools like DuckDB actually win
* The real tradeoffs behind Lakehouse architectures
* Why data modeling is still critical in an AI-driven world
* How engineers should think about building in 2026 and beyond
This is one of those conversations that helps you zoom out and see where things are actually going, not just what tools are trending this week.
If you’re building data platforms, experimenting with AI, or just trying to simplify your stack, this one is worth your time.
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This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

In this episode of the Data Engineering Central Podcast, I sit down with Ben Rogojan to talk about the real story behind data engineering careers, Big Tech, and what’s changing right now.
Ben shares how he went from working in kitchens… to data engineering… to Facebook… and eventually walking away from it all to build his own consulting business.
And yeah, it wasn’t all glamorous.
“I was making the same money as Facebook… and I hated my life.”
We get into the stuff most people don’t talk about:
* What it’s actually like working in Big Tech
* Why high-paying jobs can still burn you out
* How he transitioned into consulting (and what people get wrong)
* The reality of modern data stacks and tool sprawl
* Whether data engineering is changing because of AI
* Why fundamentals still matter more than ever
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We also go deep on where the industry is heading:
* Is the “modern data stack” breaking down?
* Are tools like DuckDB actually replacing warehouses?
* Is data modeling dead… or just not trendy anymore?
* What AI is really changing (and what it’s not)
If you’re trying to break into data, grow your career, or figure out where things are headed… this is one of the more honest conversations you’ll hear.
Data Engineering Central is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Ben also runs a course and community for those interested in getting into consulting.


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

Most companies don’t have a tooling problem. They have a foundation problem.
In this episode, I sit down with Matthew Housley, a famed co-author of Data Engineering Fundamentals and former CTO of Ternary Data, to talk about what actually makes data teams successful and why so many organizations get it wrong despite having modern stacks, cloud platforms, and expensive dashboards.
* Matthew’s path is a little different than most. He started in academia as a mathematics instructor before moving into industry as a data scientist at Overstock.com, and eventually leading data strategy and analytics as a CTO. That mix of academic rigor and real-world execution gives him a very clear perspective on where things break down.
We get into the gap between data science and real business impact, why analytics foundations matter more than flashy models, and what companies consistently underestimate when building out data platforms. We also talk about what it actually looks like to transition from academia to industry, and how that shapes how you think about data problems at scale.
Data Engineering Central is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

If you’ve ever felt like your data stack should be delivering more value than it is, this conversation will probably hit close to home.
⏱️ Topics we cover:
* Why most analytics efforts fail before they even start
* The difference between “doing data” and delivering value
* Data science vs data engineering vs analytics reality
* Academic thinking vs industry execution
* What CTOs actually care about when it comes to data
* Building foundations that don’t fall apart six months later
Thanks for reading Data Engineering Central! This post is public so feel free to share it.



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe