I fix and build data tech stacks so your reporting is easy, automated, accurate.

Nobody actively owns the data. Definitions drift between teams. Mapping tables go stale. The models that matter were built by someone who left, and the documentation is a Notion page from 2023 half the team forgot exists.

You feel it two ways. First, errors. Two reports for the same metric that don't match, dashboards nobody trusts, weekly QA calls to reconcile numbers by hand before anyone can share them.

Second, no automation. Every source change breaks a pipeline. Every new question becomes a ticket. The stack that was supposed to run itself needs a human in the loop every day to keep the numbers honest.

Pipelines break. Sources change schemas without warning. Business logic evolves. That's normal. The difference between a healthy stack and a broken one isn't whether errors happen. It's how fast you find out about them.

That means QA checks built into the models themselves, running every time the pipeline runs: row counts, uniqueness, freshness, expected ranges, error flagging. And an alerting system that pings the right person the moment a check fails, not the next time someone opens a dashboard.

The goal isn't a stack that never breaks. It's one that tells you the second it does, so you can fix it before anyone reports on a bad number.

I rewrite the models that matter, build what's missing, and fix what's silently wrong. Then I stick around long enough to hand it over properly, so your team can keep it running without me.