Data 
General Claude Code isn’t going to replace data engineers (yet)
AI will not replace data engineers tomorrow. But it is already changing how we work — including, occasionally, helping us do stupid things with more confidence.
Used badly, AI will make us weaker engineers. We outsource judgment, trust the output too quickly, miss the details, and slowly lose the habit of critical thinking. Used well, it can make us sharper. AI can help with the parts our brains are weaker at: holding lots of context, exploring many options, drafting code, explaining errors, and moving faster.
This talk is inspired by a real example: Claude Code building a dbt project on DuckDB from API data. It created models, tests, docs, incremental loads, and freshness checks. Impressive. Until you realise it missed API pagination, dropped useful fields, and made questionable modeling choices. The pipeline looked like it worked, but it did not.
AI is great at speeding up the mechanical parts of data engineering: writing boilerplate, debugging dbt errors, generating SQL, suggesting tests, and exploring unfamiliar code. But it does not remove the need for judgment.
Someone still needs to ask: Is the data complete? Is the model correct? Are the assumptions safe? Would I trust this dashboard in a real business decision?
So this is not a talk about “AI instead of data engineers.”, it is about data engineers using AI well. Not to delegate thinking, but to help us magnify our skills.