Data Engineering Central Podcast

What happens to software engineers when AI can write most of the code?
In this episode of the Data Engineering Central Podcast, I sit down with Gregor Ojstersek, former CTO, author of The Multiplier Mindset (O’Reilly), and creator of the Engineering Leadership Newsletter, which recently surpassed 200,000 subscribers.
We dive into the changing world of software engineering, why technical skills alone aren’t enough anymore, and what it takes to become a successful engineer and engineering leader in the age of AI.
Gregor shares his journey from self-taught programmer to CTO, how he built one of the largest engineering leadership newsletters, and why the most valuable engineers of the future won’t necessarily be the ones writing the most code.
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Topics we cover:
* How Gregor went from self-taught developer to CTO without a computer science degree
* Why AI is changing the skills engineers need to succeed
* The future of junior, senior, staff, and principal engineering roles
* Whether engineering management is becoming a harder career path
* Why generalists and problem-solvers are becoming increasingly valuable
* The danger of AI-generated code, poor engineering practices, and losing fundamental skills
* The ideas behind Gregor’s new O’Reilly book, The Multiplier Mindset
* How Gregor grew his Engineering Leadership Newsletter to over 200,000 subscribers
* Why slowing down and building the right things matters more than ever
Chapters
00:00 – The Multiplier Mindset01:00 – Gregor’s background and journey into engineering08:00 – Learning engineering fundamentals before AI11:00 – From software engineer to engineering leader17:00 – Why technical skills alone aren’t enough20:00 – AI, layoffs, and the future of engineering jobs27:00 – The Multiplier Mindset and writing a book with O’Reilly32:00 – Growing a newsletter to 200,000 subscribers36:00 – Junior, senior, staff, and principal engineers in the AI era41:00 – The changing path to CTO44:00 – AI-generated code and engineering quality49:00 – Why writing code was never the real bottleneck52:00 – Final advice for engineers
Find Gregor:
Engineering Leadership Newsletter:
Book: The Multiplier Mindset by Gregor Ojstersek (O’Reilly)
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What happens when you finally achieve financial independence, walk away from a successful career in Big Tech, and have the freedom to do whatever you want? Is it everything you imagined it would be?
In this episode of the Data Engineering Central Podcast, I’m joined once again by Kevin Martin, who spent nearly 13 years working at Apple before deciding he’d had enough of the corporate grind. When we last spoke, Kevin had just left Apple and was preparing to spend much of 2026 backpacking some of America’s most beautiful wilderness.
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Well, Kevin is back, and he’s got some stories to tell.
Since our last conversation, Kevin has completed hundreds of miles of backpacking, including the Benton MacKaye Trail and the legendary John Muir Trail. Along the way, he spent a terrifying night with multiple black bears circling his tent in the Smoky Mountains, experienced the incredible beauty of the Sierra Nevada, and learned some unexpected lessons about himself.
One of the most interesting things Kevin discovered was that leaving the corporate world doesn’t necessarily mean leaving behind the mindset that made you successful there. After years of working in a high-performance environment at Apple, he found himself approaching retirement and backpacking with the same intensity, planning, and expectations that he’d brought to his career.
* We dig into that realization and what it means to actually enjoy the financial freedom you’ve worked so hard to achieve.
Of course, we also wander into plenty of other topics, including the changing landscape of Big Tech, AI-driven layoffs, whether anyone’s job is really safe anymore, and the importance of building financial independence in an increasingly uncertain world.
* We talk about lifestyle inflation, the FIRE movement, the psychology of money, and why doing difficult things in the outdoors can completely change your perspective on everyday problems.
Perhaps my favorite part of the conversation is our discussion about happiness and the tendency to constantly chase the next big milestone. Whether it’s a promotion, a financial goal, retirement, or climbing a mountain, there’s always something else waiting on the horizon.
At what point do we stop chasing the next thing and actually enjoy the life we’ve built?

It’s a fun, thoughtful conversation that goes well beyond technology and money. If you work in tech, dream about financial independence, love the outdoors, or simply find yourself wondering what life might look like outside the traditional career path, I think you’ll enjoy this one.
Grab a cup of coffee and join us!
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What does it actually take to build AI systems that work in production?
In this episode of Data Engineering Central, I sit down with Maria Vechtomova, AI Engineering Lead, co-founder of CAUCHY, Databricks MVP, and author of MLOps with Databricks.
Maria has spent more than 12 years working across data, machine learning, MLOps, and AI engineering. We talk about how production ML has evolved from the early days of homegrown model platforms to today’s world of LLMs, agents, Databricks, MLflow, and AI-assisted coding.
We dig into why LLM applications can be significantly harder to evaluate and monitor than traditional ML, the growing importance of tracing and observability, and why governance becomes more complicated when agents can interact with data and other systems. Maria also explains why many of the problems we’re facing with AI aren’t actually new — AI has simply made them much harder to ignore.
* We also get into AI coding tools, testing AI-generated code, Databricks and Unity Catalog, the rapidly changing LLMOps ecosystem, and what engineers should focus on as AI changes the way software is built.
One of my favorite ideas from the conversation: AI is a mirror. It can help you write a lot more code, a lot faster — but whether that code is actually good still depends on the engineer behind it.
Maria also shares what she thinks matters most for engineers entering the field today: common sense, soft skills, and business sense.
Topics include: MLOps and LLMOps, production AI, Databricks and MLflow, AI agents, observability and tracing, model and LLM evaluation, AI governance, Unity Catalog, AI-generated code, testing, and the future of AI engineering.
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I sat down with Erfan Hesami, founder of Pipeline to Insights, to talk about where data engineering is headed as AI and agents become part of everyday engineering work.
We dig into the changing role of the data engineer, the growing overlap between data and AI engineering, and why strong fundamentals may actually become more important as AI makes writing code cheaper and easier.
We also talk about AI agents inside modern data platforms, governance and security, AI-generated code and technical debt, controlling AI costs, the importance of human judgment, and why communication and business skills are becoming increasingly valuable for engineers.
* Erfan also shares the story behind Pipeline to Insights, how writing helped accelerate his own learning, and how consistency helped him grow the publication from its first post in October 2024 into a successful data and AI community.
If you’re a data engineer, analytics engineer, AI engineer, or someone trying to break into data, this conversation offers a grounded look at what skills are likely to matter as AI changes how we build software and data systems.
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What happens when you spend decades building a successful career in tech, finally reach Big Tech, and then suddenly get laid off?
In this episode of the Data Engineering Central Podcast, I sit down with AsianDadEnergy to talk about his journey from writing BASIC on an IBM 386 and working with COBOL mainframes to consulting, becoming a chief architect in Big Tech, and eventually losing his job after seven years.
But the layoff wasn’t the end of the story.
Years earlier, the 2008 financial crisis convinced him and his wife to live below their means, eliminate debt, save aggressively, and invest. By the time his Big Tech layoff arrived, those investments had grown enough to cover their living expenses—and he eventually realized he didn’t actually need to find another job.

We also get into AI and the future of software engineering, why junior engineers may be particularly vulnerable, whether AI is taking away some of the struggle that traditionally made programmers better, and why communication and other human skills are becoming survival skills for engineers—not just skills you need to get promoted.
Finally, we talk about separating your identity from your job, building multiple streams of income, creating content on YouTube and Substack, and why there is a lot more to life than what happens in front of a computer screen.
https://www.youtube.com/@AsianDadEnergy
Big Tech. AI. Layoffs. Financial independence. Content creation. And figuring out what actually matters when the career you’ve spent decades building suddenly disappears.
I think this one will resonate with a lot of people working in tech right now.
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