using-dbt-for-analytics-engineering

Using dbt for Analytics Engineering

Core principle: Apply software engineering discipline (DRY, modularity, testing) to data transformation work through dbt's abstraction layer.

STOP — is this a breaking change to a model with consumers? Renaming, removing, or retyping a column — on a model that downstream models, exposures, or external/BI consumers depend on — is a breaking change. Do not edit it in place (that breaks those consumers the moment it deploys). REQUIRED SUB-SKILL: Use the working-with-dbt-mesh skill to roll it out with model versions (and a latest version pointer) so consumers get a migration window. Come back here for the SQL once the versioning approach is decided.

When to Use

• Building new dbt models, sources, or tests
• Modifying existing model logic or configurations
• Refactoring a dbt project structure
• Creating analytics pipelines or data transformations
• Working with warehouse data that needs modeling

Do NOT use for:

• Querying the semantic layer (use the answering-natural-language-questions-with-dbt skill)
• Breaking changes to a model with consumers (column rename/remove/retype) — use the working-with-dbt-mesh skill to version the model instead of editing in place