Stay ConnectedI’m Joe Reis, co-author of Fundamentals of Data Engineering, creator of Mixed Model Arts, international speaker, and long-time observer of the tech industry’s favorite recurring mistakes.
Weekly broadcasts from somewhere on the road. Expect raw solo rants and long-form conversations with the engineers and leaders actually building things. I dive into data, AI, architecture, and career survival.
This podcast is invitation-only. Unsolicited guest pitches and PR outreach are not considered.
Copyright: © Joe Reis
I meet a lot of people who want to accomplish major goals next year. Then the year comes and goes and most people are still waiting to get started.
It's almost December. Rather than wait until the New Year to get going, use December to plan how you'll execute on "that thing" you're itching to accomplish. Time waits for nobody, so get going.
In this episode, Ciro Greco (Co-founder & CEO, Bauplan) joins me to discuss why the future of data infrastructure must be "Code-First" and how this philosophy accidentally created the perfect environment for AI Agents.
We explore why the "Modern Data Stack" isn't ready for autonomous agents and why a programmable lakehouse is the solution. Ciro explains that while we trust agents to write code (because we can roll it back), allowing them to write data requires strict safety rails.
He breaks down how Bauplan uses "Git for Data" semantics - branching, isolation, and transactionality - to provide an air-gapped sandbox where agents can safely operate without corrupting production data. Welcome to the future of the lakehouse.
Bauplan: https://www.bauplanlabs.com/
Just launched your Substack? Great! Here’s what to do next.
This episode covers the realities of writing long-form in public, the traps that cause most writers to stall, how to build consistency, and how to grow an engaged audience from day one.
Data engineering is undergoing a fundamental shift. In this episode, I sit down with Nick Schrock, founder and CTO of Dagster, to discuss why he went from being an "AI moderate" to believing 90% of code will be written by AI. Being hands on also led to a massive pivot in Dagster’s roadmap and a new focus on managing and engineering context.
We dive deep into why simply feeding data to LLMs isn't enough. Nick explains why real-time context tools (like MCPs) can become "token hogs" that lack precision and why the future belongs to "context pipelines": offline, batch-computed context that is governed, versioned, and treated like code.
We also explore Compass, Dagster’s new collaborative agent that lives in Slack, bridging the gap between business stakeholders and data teams. If you’re wondering how your role as a data engineer will evolve in an agentic world, this conversation maps out the territory
Dagster: dagster.io
Nick Schrock on X: @schrockn
The days of easy entry into data jobs over. Maggie Wolff joins the show to discuss the new reality of the data career landscape. We dive into why the bar is higher than ever and why "cold DMing" on LinkedIn is a terrible strategy.Maggie also breaks down her secret strategy for networking as an introvert: treating events like a game or role-playing a more extroverted friend. Plus, we discuss the rise of AI in education, the problem with "lazy" learning , and why companies replacing humans with AI are making a mistake.
Matt Housley joins me for our monthly round-up of topics. This time, there's danger everywhere - The AI Bubble, how vibe coding is evolving, AI slop, and more.
After 1,500+ conversations with CDOs and VPs of data , guest Malcolm Hawker noticed a disturbing pattern: a "limiting mindset" that causes data leaders to fail. He argues that too many leaders blame external factors such as "culture" , "data literacy", or a lack of support rather than taking accountability for delivering value.
In this conversation, Malcolm breaks down how this mindset is reinforced by the analyst and consultant community and why it leads to a "value fatigue" where no one can prove their own ROI. He offers a clear path forward, starting with a simple 3-question framework for any new CDO and explains why "culture" is actually an outcome of delivering value, not a prerequisite for it.
We also discuss his new book, "The Data Hero Playbook," tackle the "AI Ready" myth , explaining why conflating it with "BI Ready" is holding companies back and why your data is likely "good enough" to start right now.
In this conversation, Dr. Cecilia Dones and I discuss the social skills we're losing as AI becomes more integrated into our lives. We explore the erosion of social norms, from AI companions joining Zoom calls without consent, endless enshitified content, to my son's generation calling AI girlfriends "clankers".Is there hope? We break down the "rage currency" that dominates media and the positive AI stories that go unheard. The biggest takeaway: as the world becomes more synthetic, "showing up" in person will become the ultimate "premium value."
In conversations I've been having with leaders and practitioners, there's some open-ended questions about the impact of AI on vendors and open-source projects. If you don't have a moat, you need to start thinking about how AI coding tools will erode the edges of your product. And what about getting users and traction? I cover this and much more in this episode. Enjoy!
Sujay Dutta and Sidd Rajagopal, authors of "Data as the Fourth Pillar," join the show to make the compelling case that for C-suite leaders obsessed with AI, data must be elevated to the same level as people, process, and technology.
They provide a practical playbook for Chief Data Officers (CDOs) to escape the "cost center" trap by focusing on the "demand side" (business value) instead of just the "supply side" (technology). They also introduce frameworks like "Data Intensity" and "Total Addressable Value (TAV)" for data.
We also tackle the reality of AI "slopware" and the "Great Pacific garbage patch" of junk data , explaining how to build the critical "context" (or "Data Intelligence Layer") that most GenAI projects are missing. Finally, they explain why the CDO must report directly to the CEO to play "offense," not defense.
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