Data platforms & pipelines
Ingestion, warehousing and transformation your analysts can trust at 9am without asking who broke it.
Most model problems are data problems wearing a disguise. We build the ingestion and transformation layer underneath: sources landed reliably, schemas that fail loudly instead of silently, and tests on the tables people actually make decisions from.
A lineage graph that answers "where did this number come from" in seconds rather than an afternoon changes how a company argues about its own metrics. So does a freshness SLA that someone owns. Both are unglamorous and both pay back within a quarter.
We are pragmatic about the stack. A warehouse and dbt covers most companies for a long time; streaming earns its complexity only when a decision genuinely cannot wait for the next batch. We will push back on architecture that is ahead of the problem.
What you get
- Batch and streaming ingestion with backfill and replay
- Warehouse or lakehouse modelling, dbt projects, incremental builds
- Data quality tests, freshness SLAs and lineage
- Orchestration with real retries, alerting and runbooks
Common questions
Do we need a warehouse, a lakehouse or neither?
Can you fix pipelines that fail silently?
Do you work with our analytics team or replace them?
Next step
Tell us what has to ship, and by when.
One call, no deck. If we are not the right team for it we will say so, and usually point you at who is.
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