CONTRIBUTING.md

Contributing to dbt-analytics-engineering

Thanks for helping improve this dbt data engineering project.

Scope

Contributions should focus on:

• dbt models, tests, macros, and documentation
• CI/CD workflow improvements
• Local development setup and onboarding
• Data quality and modeling best practices

Local setup

1. Fork and clone the repository.
2. Create a feature branch from main.
3. Copy .env.example → .env and profiles.example.yml → ~/.dbt/profiles.yml.
4. Start SQL Server: docker compose up -d
5. Install dependencies: pip install -r requirements.txt
6. Validate locally before opening a PR:

dbt deps
dbt parse
dbt debug
dbt run
dbt test

Optional SQL linting:

sqlfluff lint models

Pull request checklist

• Change is scoped and easy to review.
• Models include tests and descriptions where applicable.
• README or design docs updated if behavior changed.
• No secrets, passwords, or private endpoints committed.
• CI passes (dbt-ci workflow).

Commit and PR guidelines

• Use descriptive titles (e.g. feat: add stg_sales staging model).
• Keep one topic per PR.
• Link related issue(s) when possible.
• Include validation notes in the PR description.

What to avoid

• Do not commit profiles.yml, .env, or credentials.
• Do not remove existing layer structure without discussion.
• Do not skip tests for new models with business-critical fields.

Review SLA

• First maintainer response target: within 2 business days.
• Follow-up review after updates: within 3 business days.

For behavior expectations, see CODE_OF_CONDUCT.md.