Episode 30: The AI Paradox: Why Your Data Team’s Workload is About to Explode
Description
Chris Child, VP of Product, Data Engineering at Snowflake, joins High Signal to deliver a new playbook for data leaders based on his recent MIT report, revealing why AI is paradoxically creating more work for data teams, not less. He explains how the function is undergoing a forced evolution from back-office “plumbing” to the strategic core of the enterprise, determining whether AI initiatives succeed or fail. The conversation maps the new skills and organizational structures required to navigate this shift.
We dig into why off-the-shelf LLMs consistently fail to generate useful SQL without a semantic layer to provide business context, and how the most effective data engineers must now operate like product managers to solve business problems. Chris provides a clear framework on the shift from writing code to managing a portfolio of AI agents, why solving for AI risk is an extension of existing data governance, and the counterintuitive strategy of moving slowly on foundations to unlock rapid, production-grade deployment.
LINKS
- MIT Technology Review Report: Redefining Data Engineering in the Age of AI
- The Evolution of the Modern Data Engineer: From Coders to Architects
- Why Most AI Agents Fail (and What It Takes to Reach Production) with Anu Brahadwaj (Atlassian)
- The End of Programming As We Know It with Tim O'Reilly
- The Incentive Problem in Shipping AI Products — and How to Change It with Roberto Medri (Meta)
- Andrej Karpathy — AGI is still a decade away
- Chris Child on LinkedIn
- High Signal podcast
- Watch the podcast episode on YouTube
- Delphina's Newsletter
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