Data Engineering Central Podcast

In this episode of Data Engineering Central, I sit down with a veteran Software Engineer John Crickett; with decades of experience in the industry to unpack what really matters in building a long and successful engineering career.
We talk about how he first got into software, the early jobs and tools that shaped his thinking, and the massive technology shifts he’s witnessed across decades of engineering—from early stacks and tools to today’s AI-assisted workflows.
* We also dive into the difference between coding and real-world software engineering, what separates junior, senior, and principal engineers, and why many developers misunderstand what it takes to grow in this field.
* We discuss leadership vs individual contributor paths, the origin of his Coding Challenges platform, why algorithm puzzles dominate developer culture, and what actually makes engineers improve quickly.
Finally, we tackle the big question everyone is asking right now: how AI is reshaping software engineering, and what skills will matter most over the next decade.


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In this episode of the Data Engineering Central Podcast, I sit down with Yuki (Yuki Kakegawa) to talk about his journey into tech, the tools and platforms he’s worked with, and where he thinks data engineering and AI are headed next.
We cover:
• How Yuki got into tech• Early career lessons and pivots• Tools and technologies he’s worked with over the years• How data engineering has evolved• The impact of AI on software development• What engineers should focus on right now• Advice for those building their careers in data
Yuki shares practical insights on navigating the industry, staying adaptable, and thinking long-term about your technical growth.
If you’re a data engineer, aspiring engineer, or just interested in where AI and modern software are going, this one’s for you.
Yuki writes on …
LinkedIn - https://www.linkedin.com/in/yukikakegawa/
https://yukikakegawa.me/#blog

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In this episode of Data Engineering Central, I sit down with Bart Konieczny — data engineer, distributed systems expert, and well-known author in the Data and Spark ecosystem — for a deep technical conversation about modern data engineering.
We cover:
* How Bart got into tech and distributed systems
* His journey through different engineering roles
* Spark internals and why they still matter
* The realities of lakehouse architecture
* Streaming vs batch systems
* AI’s impact on data engineering
* What engineers should focus on in 2026
In a world obsessed with abstractions and AI tooling, we explore whether understanding the internals is still worth it — or if the game has fundamentally changed.
If you’re a data engineer, architect, or platform leader trying to navigate the next phase of the lakehouse era, this one’s for you.
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🎙️ Data Engineering Central PodcastHosted by Daniel Beach
If you’re a CTO or data leader looking for help building or optimizing your data platform, reach out — consulting inquiries welcome.
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In this episode of the Data Engineering Central Podcast, I sit down with Maxine Meurer, DevOps engineer, author, and educator behind I Love DevOps, for a wide-ranging conversation about careers, infrastructure, automation, and what it actually means to build systems that last.
This isn’t a buzzword-heavy DevOps chat. It’s a grounded, honest discussion between two engineers about how people really get into tech, how careers evolve over time, and why modern infrastructure is as much about systems thinking and human judgment as it is about tools.
We talk through Maxine’s journey from early technical curiosity to hands-on DevOps work, dealing with “ClickOps” to automation-first infrastructure, and how writing and teaching reshaped the way she thinks about engineering.
What we cover in this episode:
* 🛠️ From ClickOps to DevOps — what that transition actually looks like in the real world
* 🧠 Why DevOps is fundamentally about systems and people, not just pipelines and YAML
* 📚 How Maxine went from self-teaching to authoring practical guides like LLMs for Humans and The DevOps Career Switch Blueprint
* 🤯 Common mistakes engineers make when learning DevOps, cloud, and distributed systems
* 🔍 Testing failures, production realities, and where modern infrastructure still breaks down
* 🤖 What AI and LLMs actually change for engineers, and what’s mostly hype
* 🧭 Career advice for engineers without a traditional background
* 🔮 Where DevOps and platform engineering are heading over the next 3–5 years
Throughout the conversation, Maxine brings a refreshing, human-centered perspective to topics that are often over-abstracted or oversold. We dig into the tradeoffs behind tooling choices, the reality of production systems, and the importance of learning how to think, not just what to deploy.
If you’re navigating a DevOps or infrastructure career, wrestling with modern stacks, or trying to make sense of AI’s role in engineering, this episode offers clarity, context, and hard-won insight.
Learn more about Maxine’s work:
* Writing & guides:
* LinkedIn: https://www.linkedin.com/in/maxinemeurer/
* Gumroad resources: https://mameurer.gumroad.com
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In this episode, I sit down with industry veteran Robin Moffatt — Sr. Principal Advisor in Streaming Data Technologies (Kafka, etc.) and a longtime voice in the data engineering community, to unpack the journey from old-school data architectures to today’s real-time streaming ecosystems. From early mainframe data processing and COBOL through the rise of Apache Kafka, streaming ETL, and event-driven systems, Robin shares lived experience from across decades of building, scaling, and evolving data platforms.
We dive into:
* 🧠 How the role of software engineering has shifted with the rise of distributed, real-time systems
* 📊 Why event streaming and platforms like Kafka aren’t just messaging systems, but the backbone of modern data architectures
* 🚀 How the community’s tooling and mental models have had to evolve — from static databases and nightly jobs to continuous, always-on streaming applications
* 🤖 A candid look at how AI and real-time data are intersecting, shaping both tooling and expectations for the next decade
* 🔮 Robin’s perspective on where the industry is headed — beyond buzzwords toward real engineering maturity
Along the way, we get historical context, real-world lessons from conference stages and community forums, and a perspective on building resilient, scalable systems that power today’s data-rich applications.
If you’ve ever wondered how we got from batch jobs to continuous event streams, or what it really takes to build modern pipelines that support AI workflows, this conversation with Robin is a must-listen.
For more from Robin:
* 📍 His personal blog & talks:
https://rmoff.net/
* 🔗 LinkedIn profile: https://www.linkedin.com/in/robinmoffatt
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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