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

About this episode

The episode discusses how agentic AI is transforming data engineering and the implications for productivity and the role of data engineers.

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…

People in this episode

Host: Tobias Macey

Guest: Gleb Mezhanskiy

Topics covered

• AI in data engineering
• productivity gains
• agentic workflows
• data stack consolidation
• AI adoption
• data quality

Keywords

• AI-first
• data engineering
• SQL
• dbt models
• autonomous agents
• data products
• Jevons paradox
• AI-driven software delivery
• data quality
• cross-functional skills

Mentioned in this episode

Organizations: Datafold

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