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Data & analytics

One version of the numbers. We build pipelines that pull data from your tools into one warehouse, model it so finance and product agree, and put it in dashboards people trust enough to act on.

Typical length
3 to 10 weeks
Team
Data engineer and an analyst
Starts with
A list of the questions you cannot answer today

If any of these sound familiar.

What you get.

How it runs.

  1. Step 1

    Question

    We start from the decisions you need to make, not from the tables you have.

  2. Step 2

    Connect

    Sources flow into the warehouse on a schedule, with tests.

  3. Step 3

    Model

    Shared definitions agreed with finance, product and operations.

  4. Step 4

    Show

    Dashboards and alerts, plus training for the people who use them.

Tools we reach for

  • dbt
  • BigQuery
  • Snowflake
  • PostgreSQL
  • Airbyte
  • Metabase
  • PostHog

Plain SQL and tested models, so your analysts can read and change what we build.

Questions about this work.

Which warehouse should we use?

It depends on your volume and tools. For most small and mid-sized companies PostgreSQL or BigQuery is enough.

Is our data ready for AI?

Often not yet. Clean, well-defined data usually matters more than the choice of model.

How do you handle personal data?

We collect only what is needed, document where it lives and agree retention periods with you.

Tell us what you are building.

A short call, a clear next step. We reply within one working day.