What we build
Most data problems are not modelling problems — they are plumbing problems. The dashboard is wrong because a pipeline failed quietly, the numbers do not reconcile because two systems disagree, the report is a day stale because nothing was watching freshness. We build the layer underneath: ingestion, transformation, warehousing and the checks that make the output trustworthy.
That covers batch and streaming ingestion, transformation in a warehouse or lakehouse, orchestration, and the modelling that turns raw events into tables a team can actually query with confidence. We work with the tools you already have where they fit, and are candid when they do not.
How we approach it
We design for correctness first. A pipeline that is fast and wrong is worse than no pipeline, because people trust it. So we treat data like code: transformations are tested, schemas are explicit, and changes are reviewed before they reach anyone downstream.
- Data contracts between producers and consumers, so a schema change upstream cannot silently break a report downstream.
- Tested transformations — freshness, volume, uniqueness and referential checks that run with the pipeline, not as an afterthought.
- Observability and lineage, so when something does go wrong you can see what, where and how far it spread — in minutes, not days.
- Idempotent, replayable jobs, so a failed run is a retry, not an incident.
What you are left with
A data platform your team can operate without us: documented, version controlled, and observable enough that the first person to know about a problem is you, not a stakeholder. If you are weighing up a build, read our note on knowing whether a data pipeline is actually trustworthy — or tell us what you are working with.