dbt, from someone who moved a fintech onto it.
I've led analytics engineering on dbt at Trustly, MileIQ, Loft and Salve — including the migration off Spark. If your dbt project is slow, fragile, or has not started yet, that is the work.
What the work looks like
- migration
- Spark jobs, stored procedures or notebooks moved to SQL-first dbt. I ran exactly this at Trustly, across teams in several countries: fewer errors and faster deploys, on software engineering practice.
- modelling
- Staging, marts and tests your analysts can maintain themselves. Naming, layering and contracts that survive people leaving the team.
- rescue
- An existing project with hundreds of models, no tests and a run nobody trusts. Triage first, then a plan you can ship in weeks rather than quarters.
- enablement
- Your team owns it when I leave: documentation, review habits and CI that catches a broken model before production does.
Where it runs
The warehouse is your call — the modelling practice travels.
- warehouses
- Snowflake · BigQuery · Databricks · Redshift
- orchestration
- Airflow · Astronomer certified (Airflow 2) · dbt Cloud
- downstream
- Looker · Tableau · Omni Analytics
Why me
A decade in data, and the last four years almost entirely in dbt: leading the analytics engineering team at a global fintech, modelling financial products on Snowflake, and modernising the stack at Salve today. The person who scopes your project is the one who writes the models.
See the full track recordThe first conversation costs nothing.
Tell me what you want to build and I'll tell you how I'd do it. Reply within one business day.