High Signal: Data Science | Career | AI Episode 30: The AI Paradox: Why Your Data Team’s Workload is About to Explode
24 snips
Dec 11, 2025 Chris Child, VP of Product at Snowflake and author of an MIT Technology Review report, dives into the paradox of AI increasing data teams' workloads. He discusses how data engineering is shifting from backend functions to vital business strategy. Chris highlights the need for data engineers to think like product managers and the challenges faced with LLMs lacking business context. He emphasizes the importance of investing in foundational governance and exploratory experimentation to navigate rapid AI changes effectively.
AI Snips
Chapters
Transcript
Episode notes
Using RAG To Join Contracts With Revenue
- A customer used RAG over contracts and joined results with Snowflake revenue to answer a question once infeasible.
- That effort forced data engineers to catalog, process, and govern lots of unstructured data.
Design Data For Future Agent Queries
- Shift data engineers from writing low-level code to architecting data and anticipating future queries.
- Design data formats and structures with AI agents and future questions in mind.
Semantic Models Are The Missing Context For LLMs
- LLMs can produce syntactically correct SQL but lack company-specific business context.
- Supplying semantic models (DBT, LookML) fixes many failures by encoding meanings like "customer" or "revenue."