Analytics Engineer

What you learn

Raw data transformed into models an organisation can trust
Software engineering practice applied to analytics work
Definitions owned centrally so numbers stop disagreeing
Data quality tested rather than assumed

What you will understand

The modelled layer as a product with real consumers
One metric definition, owned in one place, used everywhere
Tests on data as well as on the code that transforms it
Why two dashboards disagree, and whose job it is to fix that

How you will practice

Model a messy source into a layer an analyst can query without asking questions
Write tests that catch a data quality failure before a dashboard shows it
Trace a number on a dashboard back through every transformation to its source

What you will build

A tested, documented modelled layer that analysts build on without you
A metric definition set that resolves a real disagreement between two teams

The proof you build

Evidence that you can build a data layer an organisation trusts enough to make decisions on.

Your work is observed with your consent, scored for independence and assistance, and turned into proof that carries a confidence level. The career path can reach a high-assurance credential, anchored by a scored capstone.

See how proof works

What is not live yet

The desktop app, consent-based observation, scoring, and credentials are in development. Nothing here implies they are live yet.

Get early access