Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show.
dbt-using-dbt-for-analytics-engineering/SKILL.md
---
name: using-dbt-for-analytics-engineering
description: Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes.
allowed-tools: "Bash(dbt *), Bash(jq *), Read, Write, Edit, Glob, Grep"
user-invocable: false
metadata:
author: dbt-labs
---
# 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:**