Your data platform works — but nobody can tell you why it breaks, or what it will cost you next quarter.

The situation

You have a data platform that grew organically. Models nobody fully understands, notebooks and pipelines with redundant logic that quietly got slower every quarter, orchestration that fails silently at 3am, a warehouse bill that keeps climbing, and a general sense that one wrong migration could take the whole thing down. It works — until it doesn’t — and no single person can hold the whole architecture in their head anymore, whatever it’s actually built on.

What the Health Check does

I treat your platform the way a structural engineer treats a building: a systematic pass across every load-bearing layer, documenting what’s sound, what’s fragile, and what will fail first under growth — across the Azure data stack broadly, not one tool in isolation. If you’re on Databricks, that means notebook and job design, not just dbt models — performance, redundant logic, and cluster or compute waste. If you’ve built a medallion or lakehouse layering, I check whether the bronze/silver/gold structure is actually earning its keep, or just adding hops nobody’s questioned.

What you walk away with

• A findings register ranking every issue by severity and blast radius, so you fix the things that will actually hurt you first.
• A dependency map of how data flows through your platform, so the next migration isn’t a leap of faith.
• A remediation plan scoped into work your own team can execute — no dependency on me to fix what I find.