Data pipelines that survive contact with reality

We help small and mid-sized teams move, clean and model their data without buying a platform they will outgrow in a year.

Ingestion

Batch and streaming loads from operational databases, APIs and flat files. Idempotent by default.

Modelling

Warehouse layers your analysts can actually read, with tests and documentation that stay current.

Orchestration

Scheduling, retries, alerting. Failures surface within minutes, not at the Monday meeting.

Cost review

Warehouse spend audits. Most engagements find between 20% and 40% of avoidable compute.

How engagements usually run

A short discovery week, then two to eight weeks of build with a working increment every Friday. We write the runbooks and hand them over; no lock-in to us.

Stack

Mostly Postgres, ClickHouse, dbt, Airflow and plain Python. We are deliberately boring about tooling.

Enquiries

See services for scope and rates, or read the notes for how we think about this work.