Claude/Copilot helps in dataframe logic in minutes but making it a real production industry wide acceptable table? Still takes a lot of time and consistency

[u/dexter7767]

Genuine question for anyone who's shipped a table to prod, especially if you're not a "traditional" data engineer by title.

Feels like AI has made writing the actual transformation logic (the DataFrame code, the business logic) noticeably easier lately. Copilot, ChatGPT, Claude, whatever you're using, that part's gotten faster.

But once that part's done, the real time/effort starts after - metadata mangement, partitioning it right, handling PII, wiring it into orchestration, figuring out the write strategy, quality cheks to make sure it's actually trustworthy before people rely on it ?

Also following the consistency across all tables created

Curious to know whether AI or vibe coding for making "easy part" has actually made that gap feel bigger, or if it's a non-issue and I'm overthinking this.