From Data Analyst to Analytics Engineer

What this course covers

Analytics engineering turns raw operational data into datasets that other people can use and maintain. The work includes defining the grain, writing transformations, documenting assumptions, checking the output, and reviewing changes.

It sits between analysis and data engineering. You still work from a business question, but you also own the code, tests, lineage, and delivery process behind the answer.

The role of Bruin in this course

The exercises use Bruin as the example tool. It gives the course one environment for SQL, Python, ingestion, checks, environments, and MCP-assisted work.

The skills transfer to any analytics engineering tool. Whether you use Bruin, dbt, Dataform, or another tool, you still define a model before writing it, keep the model focused, make repeat runs safe, and review generated SQL before it changes shared data.

Skills covered

• Model grain, primary keys, and business definitions
• SQL transformations and environment-aware development
• Materialization, incremental runs, and full refreshes
• Quality checks and lineage
• Version control and pull-request review
• Scoped agent-assisted work and validation

How to use the course

The reference lessons explain the terms and decisions. The linked guides show them in a Bruin project. Work in a branch, use a development environment, and follow the sections in order.