Modelled, not just moved
Dimensional models and a semantic layer, so definitions live in one governed place.
04 — Data Platform
Data Engineering & Analytics
One set of numbers, and everyone agrees on where they came from.
Dashboard proliferation is a symptom. The disease is that finance, operations and product each compute the same metric a slightly different way. We fix it at the layer where it belongs — a governed semantic model, versioned in Git, tested on every change, with lineage from source system to executive slide.
We build for the analysts who will inherit the platform, not for the diagram. That means readable SQL, documented models, sensible naming and a contribution workflow your own team can follow on week one.
What you get
- Data platform architecture & tooling selection
- Ingestion and transformation pipelines
- Governed semantic layer & metric catalogue
- Executive and operational dashboards
- Data quality monitoring & documentation
How we approach it
The parts that usually go wrong.
And what we do differently so they do not.
Tested like software
Freshness, uniqueness and referential tests running on every pipeline execution.
Lineage end to end
Column-level traceability from source table to the number on the board pack.
Self-serve that holds up
Curated marts and metric definitions analysts can extend without breaking trust.
Questions
Before you ask.
Usually not. Most of the pain sits in modelling and governance, not storage. We will tell you plainly if the platform itself is the constraint — and if it is, we will show the numbers behind that recommendation.
First governed dashboard typically lands in four to six weeks, covering one domain end to end. We deliberately go narrow and deep first rather than ingesting everything and modelling nothing.
Have a problem in this area?
Bring us the messy version. We are more useful before the requirements document exists than after it has been signed off.