Data Engineering

dbt Best Practices: Lessons from 100+ Projects

MT

Michael Torres

Analytics Lead

Dec 5, 2025
10 min read

# dbt Best Practices

Scaling dbt from a few models to thousands requires a disciplined approach to structure and testing.

1. Modular Architecture Always follow the staging -> intermediate -> marts layering. * **Staging**: 1:1 with source data, minimal cleaning. * **Intermediate**: Complex joins and shared logic. * **Marts**: Business-ready entities (dim_users, fct_orders).

2. Documentation is Mandatory If a model isn't documented, it doesn't exist. Use dbt's doc blocks to ensure every column has a description.

3. Automated Testing Every model should have at least `unique` and `not_null` tests on its primary key. Supplement this with custom data tests for business logic.

4. Version Control Treat your SQL like software. Use pull requests, code reviews, and CI/CD pipelines to ensure data quality remains high.