Introduction
I’ve been a BI engineer for over 7 years now, and the way I work today looks nothing like how I worked even two years ago.
It was: pipeline breaks, I get paged, I sift through logs, I fix it and move on. Stakeholder asks a question, I write a query, I build a chart, I send it. Repeat and rinse. The tools got better over time — we moved from cron jobs to Airflow, from Excel to QuickSight — but the process was still essentially manual. I was the bottleneck.
Then I started experimenting with AI agents in my actual day-to-day work. Not the “ask ChatGPT to write me a SQL query” kind of AI usage (though that has its place). I mean giving an AI agent access to my data warehouse, my orchestration tools, my email system and letting it autonomously investigate, validate, and act on data.
I was surprised by the results. Things that used to take me half a day like investigating why a metric dropped 15% or tracking down which upstream table broke my pipeline now take minutes. Not due to the AI being more intelligent than me, but due to its ability to verify 20 hypotheses in parallel while I’m still on my first coffee.
