Plumbers of Data Science

SQL, Python, and one cloud platform. That's what a post I saw claimed makes a good engineer. I don't fully agree, so in this episode I break down what actually makes you a good data engineer or platform architect.It starts with understanding how a data platform actually works: data sources, processing, storage, visualization, and the goals sitting on top of all of it.Once you understand that, the tools make sense, because you can see which category each tool belongs to and how they fit together (I cover this in my Skills and Tools Guide, linked below).Beyond that, I go through the technical fundamentals that actually separate good engineers from the rest:- Infrastructure (servers, SaaS, containers)- CI/CD- Orchestration- Observability and monitoring- Documentation- Problem-solving (finding the actual root cause, not just patching symptoms)- Identity and access management- Cost managementAnd then there's the part most engineers underestimate: communication. I break down the 5 types of people you'll talk to as an engineer, project management, analysts, customers, related teams, and internal management, and why you need to talk to each of them completely differently.Download the free Skills and Tools Guide!Go to https://learndataengineering.com/ and scroll down to the free learning content section.Let me know in the comments what you think makes a good engineer, and what you'd add to this list.

AI has changed engineering a lot, so in this short solo episode I think through where it's actually landed: development, debugging, optimization, and the AI engineering hype.
The honest take: AI writes the code now. Our tests, our docs, root causes, query plans that used to need years of experience. Copilot should just be called Pilot. And data modeling? Not the big deal people make it out to be.
So if the doing is handled, what's left? Knowing what to do, not doing it. We're the copilots now.
I also get into the RAG, MCP, and agents hype, and why I'm not convinced most companies need that yet.
How has AI changed the way you work, and what should I create next? Let me know in the comments.

Is AI actually making us better engineers, or quietly making us worse? I sat down with Dan Beach from Data Engineering Central to talk honestly about what it feels like to build data systems in the age of Cursor, Claude, and the so-called 100X engineer.
Dan uses AI every day, but he also admits something a lot of us feel and don't say out loud: he misses coding by hand. We got into the whiplash of loving AI one day and feeling like your skills are atrophying the next, why "vibe coding" gets the ticket done but won't save you when things break, and why the hardest parts of the job, system design, architecture, and business context, are still the parts AI can't do for you.
From there we went wide. Why data platforms keep dumbing everything down and what that does to your cloud bill, why on-prem and tools like Exasol aren't going anywhere, and why I think data modeling is one of the most overrated topics in data engineering.

▶️ Follow Dan on Instagram: https://www.linkedin.com/in/danieldavidbeach/
▶️ And Data Engineering Central on Substack: https://dataengineeringcentral.substack.com

▶️ Say hello on Instagram: https://www.linkedin.com/in/andreas-kretz/

In this podcast episode, I’m talking with Vadym Kazulkin, AWS Serverless Hero and Principal Cloud Architect.
He’s part of the AWS community for years now, speaking at events, testing new features early, and helping the community grow.
We talked about:
How serverless is being used in the real world
Why Java still has a place in modern cloud setups
What stops big companies from going all in
How AWS is evolving — and what’s missing
And what engineers should know before going “cloud-native”
 
If you're building in the cloud or just trying to make smart decisions in a fast-moving space, this episode has a lot for you.
Connect with Vadym via LinkedIn: https://www.linkedin.com/in/vadymkazulkin/

In this episode, I’m joined by Christina Stathopoulos, a former Googler who now works independently as a data & AI evangelist, trainer, and advisor.
Not only did we talk about her path from data engineering into data science, and eventually into teaching and strategic consulting. We also covered:
Why many companies still struggle with data & AI basics
The hype vs. reality of generative AI (and how to cut through the noise)
Why soft skills like communication, critical thinking, and storytelling are underrated in tech
How regulation in Europe might be slowing things down, and why AI won’t wait for borders
The difference between online and in-person learning (and why human connection still matters)
and much more!
All in all: A thoughtful, honest conversation full of insights on AI, data culture, and the human side of technology.

Get in touch with Christina via LinkedIn:
https://www.linkedin.com/in/christinastathopoulos/