Summary
In this episode, I sit down with Gleb Mezhanskiy, CEO and co-founder of Datafold, to explore how agentic AI is reshaping data engineering. We unpack the leap from chat-assisted coding to truly agentic workflows where AI not only writes SQL and dbt models but also executes queries, debugs, runs tests, and ships production-ready outcomes. Gleb explains why teams that master this AI-first loop can see 10–50x gains, how security/compliance concerns can be addressed with platform-native LLM endpoints, and why the role of data engineers is shifting from code authors to operators of autonomous agents. We dig into the consolidation of the modern data stack, the economics driving more data products (Jevons paradox), and why product thinking, domain knowledge, and cross-functional skills will define the next wave of standout data professionals. We also cover practical steps for leaders and ICs: modernizing off legacy platforms, establishing safe AI adoption paths, codifying reusable “skills” and context for agents, and building validation utilities that keep the inner loop fast and trustworthy. Finally, Gleb shares how Datafold moved to fully AI-driven software delivery and why “outcomes over tools” is the emerging model for complex initiatives like data platform migrations—and how this reframes data quality for the AI era, emphasizing broad data access plus rich context over brittle human-centric tests.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm bringing back Gleb Mezhanskiy to talk about our predictions for the impact of AI on data engineering for 2026

Interview

Introduction
How did you get involved in the area of data management?
What are the concrete steps that teams need to be taking today to take advantage of agentic AI capabilities?
What are the new guardrails/constraints/workflows that need to be in place before you let AI loose on your data systems?
How do you balance the potential cost savings and productivity increases with the up-front investment and variability in inference spend?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

The AI-First Data Engineer: 10–50x Productivity and What Changes Next

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