Where we plug in

You have data but no one trusts it. Numbers disagree across reports. New analysts take months to onboard because the schema is undocumented and the joins live in someone's head. We build the layer that fixes that.

Deliverables

  • Source-of-truth dimensional models
  • Tested, documented dbt project
  • Semantic / metrics layer
  • Internal data catalog & lineage
  • Data quality & freshness monitoring
  • API endpoints for downstream apps

Typical engagement

6–12 weeks. Often runs in parallel with modernization or analytics work.

FAQ

Common questions

What does a data transformation engagement actually produce?

A tested, documented dbt project with source-of-truth dimensional models, a semantic/metrics layer, and data quality monitoring. The bar we build to: an analyst can pull a number and ship it without cross-checking other sources first.

We already have pipelines. Why do numbers still disagree across reports?

Usually because the business logic (which orders count as revenue, when a customer counts as churned) is duplicated across dashboards instead of defined once in a modeled layer. We move those definitions into version-controlled, tested models so every report pulls from the same answer.

Do you work with our existing dbt project or start over?

We work with what you have when it's salvageable, which it usually is. A short audit tells us whether to refactor or rebuild, and we'll show you the reasoning either way before any SOW is signed.

How long does this take?

6–12 weeks for most scopes, often running in parallel with modernization or analytics work. Weekly demos, so you see the models and documentation grow rather than waiting for a big reveal.

Working on something like this?

Tell us what's going on in a few sentences. A partner (not a salesperson) reads it and replies within one business day with a concrete next step, whether or not we're the right fit.