Do Data Roles Have a Future?

Does data engineering have a future? What about data roles in general? As AI makes it faster and cheaper to write code, build pipelines, and generate analytics/reports, what happens to the people doing the work?In this episode of Freestyle Friday, I rant about how AI is reshaping data engineering, analytics, data science, and other data roles. We'll look at the changing economics of technical work, why fundamentals and business understanding still matter, and what this means for the next generation of data professionals.The work isn't disappearing, but the roles built around it will likely look very different.

Traditional drag-and-drop BI is facing an existential crisis. In this live fireside chat at Hex Prompt, I sit down with Barry McCardel (Co-founder & CEO of Hex), right as Hex announces a major pivot: Hex 4.0, a ground-up architectural rewrite built for a world where AI agents write code and traditional dashboards are turning into sand.Barry breaks down the hard realities facing data teams today:- Why wrapping legacy platforms in "agent harnesses" hit diminishing returns - How "vibe coding" across the enterprise is breaking the old centralized BI model - Why the future of analytics isn't drag-and-drop UI, but code with guardrails, context, and observability - The real KPIs modern data teams should track (eval pass rates vs. raw activity) - The emerging challenge of "cognitive surrender" when stakeholders stop critically evaluating data Whether you are an analytics engineer, data analyst, or data platform leader, this conversation offers an unfiltered look at what it takes to adapt when AI shifts the underlying substrate of data work from UI to code.

Tristan Handy (dbt/fivetran) joins me to discuss the tectonic shifts reshaping data teams, analytics engineering, and business intelligence in the age of agentic AI. Tristan opens up about stepping back into "builder mode" after years as CEO, the parallels between the dot-com era and today's AI wave, and why data teams need to stop clinging to yesterday's work, such as dashboard maintenance and SQL ticket queues.We also chat about the reality of context engineering, why traditional BI and point-and-click tools face an existential reckoning, how dbt charts approaches agentic abstractions, and why data practitioners must become deeply embedded business partners rather than pure pipeline mechanics.

The old data industry is dying. The new one hasn't quite been born. And in between, we're seeing some strange things.In this episode, I explore the Great Data Interregnum, a period when the playbook, practices, and architectures of yesterday are losing their coherence, but whatever replaces it remains uncertain.

In this episode of The Joe Reis Show, I sit down at my house in Salt Lake City with Kyle, founder of Credible Data. Before founding Credible, Kyle spent nearly 17 years at Google building core distributed systems—including the in-memory shuffle/join backplane that powers BigQuery—before moving to Gradient Ventures and spending four years helping integrate Looker into GCP. We dive deep into the reality of semantic layers, the origins of Malloy, and why the current hype around semantic layers often misses the mark. Kyle breaks down why SQL conflates data modeling and queries, how agents are shifting data work from painful bottom-up ETL to deterministic top-down domain modeling, and why giving an LLM an entire enterprise schema creates nothing but "data slop." We also get into the future of data engineering, what analysts should actually be learning today, and why unlocking the "long tail" of dark business data is where the true value lies. Connect with Kyle & Credible: credibledata.com G​uest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.

Ever feel like you've been lied to?In Scott’s Tots, Michael Scott promises a class of third graders he’ll pay their college tuition if they finish high school. Ten years later they’ve held up their end, and he shows up to tell them he's broke.A lot of people feel like those kids right now. They got the degree, learned to code, and followed the career plan they were handed, only to find a job market that feels empty and hollowed-out.In this episode I use Scott’s Tots to look at who made these promises, why most of them were sincere but wrong, and what the consolation prizes look like today. Guest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.

In this episode, I sit down with Doug Meil, software veteran and author of The Rise and Fall of Explorys and IBM Watson Health. A decade ago, IBM Watson was the undeniable face of the modern AI revolution. Fresh off its historic Jeopardy win, IBM promised Watson would revolutionize entire industries and cure cancer. Instead, it became one of tech history’s most high-profile corporate implosions. Doug lived through the heart of it. His healthcare data startup, Explorys, was acquired in 2015 to serve as a foundational pillar for IBM Watson Health. What followed was a masterclass in what Doug calls the "paradox of inorganic growth"—an unravelling driven by corporate hubris, aggressive internal "blue washing," brutal cloud lock-ins, and leadership prioritizing marketing slogans over iterative product engineering. We dig deep into the untold engineering and product side of the acquisition, why enterprise mergers crush agile technical teams, and the enduring lessons today’s AI founders and leaders must take away before jumping headfirst into enterprise AI hype.Get Doug's book here: https://link.amazon/B05ATbGBLGuest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.

A lot of people ask me for career advice. My latest advice is learn the fundamentals, build useful things that interest you, and stop outsourcing your direction to everyone around you. But originality doesn’t automatically pay the bills, copying is part of learning, and “do what AI cannot” is lazy advice. In this episode, I unpack the advice, its limits, and how to find your own way without betting your livelihood on it.Guest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.

The data industry rarely moves in a straight line. We swing from centralized to decentralized, batch to real-time, general-purpose to specialized, abstraction to control, and governance to self-service.In this episode, I unpack why these cycles keep repeating, what forces push the pendulum in each direction, and why every successful architecture eventually creates the conditions for its own backlash.

We see a lot of hype cycles in our industry - Data mesh. Generative AI. Agents. Now context. Every cycle brings a new label and a familiar promise: this will finally make your organization’s data useful.The technology has improved enormously. So why do the same problems keep coming back?In this episode, I explore how communication, incentives, and organizational politics shape what our tools actually deliver. Will context finally deliver the silver bullet to solve our problems? Is buying the next platform easier than resolving the messy organizational problems we all deal with? Listen to get my thoughts.