Service
Automation & DataOps
Every hour an analyst spends fixing a broken export is an hour not spent answering a business question. Automation and DataOps replaces the manual routine with pipelines that run on a schedule, test themselves and tell an engineer when something is off. The result is automated reporting your team can trust: data that is fresh and checked before anyone sits down in the morning.
What gets built
Transformations are built with dbt on BigQuery or Microsoft Fabric. Every business rule lives in version-controlled, tested code instead of in someone's head or a fragile chain of spreadsheets. Cleaning happens once, inside the pipeline, so the hours your analysts currently lose to fixing data every week go back into actual analysis.
Orchestration runs on Airflow or Fabric pipelines. Every run is scheduled, every model is tested before it reaches the Gold layer, and freshness checks watch the data as it moves. When a test fails, the alert goes to the engineer first, never to your stakeholders. A dashboard that is quietly wrong costs more than one that is loudly late, so the pipelines are built to fail loudly and recover fast.
Costs stay predictable. Warehouse usage is monitored, heavy models run on a deliberate schedule instead of on every refresh, and you can see what each part of the platform costs. Automated reporting should save money as well as hours, and it only does that when someone designs for it.
How it works in your team
I work embedded in your team, end to end. The pipelines are built in your repositories, reviewed with your engineers if you have them, and documented with runbooks if you do not. You see the first automated reports early, then coverage widens source by source. At handover your team owns a system it can read, run and extend: every schedule, test and alert is written down, and nothing depends on me being around.