Service
Lakehouse architecture
Most teams do not have a data problem. They have five copies of the same number and no way to say which one is right. A lakehouse gives you a single source of truth: one governed place where every report, dashboard and model reads the same data. It does the work of a data warehouse and a data lake in one platform, without locking you into either.
What gets built
The core is a medallion design on Microsoft Fabric or BigQuery. Raw data lands in Bronze exactly as it arrives, gets cleaned and tested once in Silver, and is modeled into business terms in Gold. Your teams stop debating whose spreadsheet is right, because there is one governed layer everyone reads from. If you already run a data warehouse, it fits into this design rather than being thrown away.
Governance is not a phase two. Purview on the Microsoft side or Dataplex on Google Cloud is wired in from the first table: who can see what, where each number came from, and what breaks if a source changes. On top sits a semantic layer, so revenue means the same thing in every dashboard and in every AI tool that reads your data.
Everything is documented and transferable. Tables live in open formats like Delta and Iceberg, the code lives in your repositories from the first commit, and the architecture document is written so anyone on your team can read it. Nothing about the platform depends on me once the engagement ends.
How it works in your team
I work embedded in your team, end to end. The person on the intake call is the person who designs, builds and hands over, so nothing gets lost between a sales deck and a delivery team. You see progress weekly in your own repositories, decisions are written down as they are made, and I add value where your team needs it most: sometimes that is architecture, sometimes it is walking your analysts through the new platform. At handover you get the code, the docs, the runbooks and the keys, and your team runs it.