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Data Strategy & Engineering

Without trustworthy data, AI is just confident guessing.

AI is only as good as the data underneath it. We do the unglamorous work of getting your data right first. One trustworthy source, clear owners, quality checks where the data is created, and rules that hold up to an audit.

Why data programs stall
Pipelines nobody owns.

Twelve teams, sixty pipelines, one breaks on a Tuesday morning. Four hours of detective work to find out who built it three years ago. Then four more to fix it.

Bad data caught at the dashboard.

By the time bad data shows up in a leadership review, it has been building up for weeks. Quality has to be checked where the data is created. Not where someone notices it.

Rules bolted on at the end.

Tracking, access controls, and personal data handling added to a warehouse that is already live is the most expensive way to do it. We build them in from the start.

What we build

Four pieces of a real data foundation.

Data platform
One trustworthy place for your data.

Snowflake, BigQuery, or Databricks, designed for the questions your business actually asks. Not the example setup from a vendor sales deck.

Clear ownership
Every dataset has an owner.

The team that produces the data agrees what it should look like and stands behind it. Breaking changes are agreed in advance, not sprung. The teams that use the data stop being surprised.

Quality at the source
Tests where the data is born.

Checks running at the point the data is created. Not last-resort firefighting downstream. Problems get caught before they spread to anywhere they matter.

Governance
Tracking, access, and rules built in.

Open data tracking, access tied to identity, and rules the security team can audit without a meeting. Built in from day one, not retrofitted under regulatory pressure.

How the data flows

Clear owners. One trustworthy path.

Service A Service B Service C AGREED RULES Format · Promise Owner · Checks ONE SOURCE Snowflake Analytics ML / AI Reporting QUALITY CHECKS · TRACKING · ACCESS RULES · MONITORING
Fig. 10 · How the data flows from source to use
How we approach it

Four things we insist on.

01
Treat data like a product.

Every dataset has an owner, a promise, and a roadmap. If it does not, it does not belong in the warehouse.

02
Quality at the source.

The teams producing the data are responsible for what they produce. Firefighting downstream is a symptom of a broken system, not a strategy.

03
Rules from day one.

Tracking, access, and personal data handling built in from the start. Not added in a panic when the regulator calls.

04
Boring tech, on purpose.

Postgres, Snowflake, dbt, Airflow. The clever choices are saved for the parts of the system that need them.

The default stack
Warehouse & lake
  • Snowflake
  • BigQuery
  • Databricks
  • Postgres
  • S3 / GCS
Modelling & orchestration
  • dbt Core / Cloud
  • Apache Airflow
  • Dagster
  • Prefect
Quality & contracts
  • Great Expectations
  • Soda
  • Monte Carlo
  • Data Contracts (open spec)
Governance
  • OpenLineage
  • Unity Catalog
  • Atlan
  • Collibra
Let's talk

Ready to engineer your industrial future?

Tell us what you're building and we'll tell you, honestly, how we'd approach it. We read every message and reply within one business day.