Showcase Your dbt Skills with a GitHub README Badge

Use this markdown in your README:

![dbt](https://img.shields.io/badge/dbt-FF694B?style=for-the-badge&logo=dbt&logoColor=white)

The #FF694B is dbt's signature orange-red from their official brand. The dbt logo identifier renders dbt's logo from Simple Icons. This warm orange-red badge pairs naturally with data warehouse badges (Snowflake, BigQuery) and Python in a data engineering or analytics engineering profile.

dbt proficiency has clear progression milestones:

• Basic: Writing models (SQL SELECT statements), understanding the ref() function for model dependencies
• Intermediate: Sources, seeds, and snapshot models; generic and singular data tests; generating documentation with dbt docs generate
• Advanced: Custom macros (Jinja templating), packages from dbt Hub, incremental models for large datasets, exposures
• Architecture: dbt project structure — layered model architecture (staging → intermediate → mart layers), tagging strategy, multi-environment deployment

Knowing when to use incremental models (for large fact tables) versus full-refresh models (for dimension tables) is a practical dbt skill that distinguishes engineers who have used dbt on real data volumes from those who have only used it on tutorial datasets.

dbt sits in the transformation layer of modern data stacks:

• Modern data stack: dbt + Snowflake/BigQuery/Redshift + Airflow + Python
• dbt Core setup: dbt + PostgreSQL + GitHub Actions for CI
• Analytics engineering: dbt + Looker/Metabase + Snowflake + Fivetran
• DataOps: dbt + Airflow + Great Expectations + Tableau

dbt Core (open source) vs. dbt Cloud is worth distinguishing in your profile description. dbt Cloud users work with a managed CI/CD pipeline, while dbt Core users self-manage orchestration. Both are valid production setups — mentioning which you use helps context-match your experience to specific roles.

dbt projects are SQL and Jinja-templated SQL files — GitHub detects these as SQL in your language statistics. Python appears in dbt-utils custom macros or analysis scripts. A repository with clear dbt project structure (models/, tests/, macros/, seeds/, snapshots/) immediately communicates analytics engineering competence to any technical reviewer.

For pinned repositories, a dbt project with documented models (YAML description: fields), passing data tests, and a generated dbt documentation site is an outstanding analytics engineering portfolio piece. Few analytics engineers maintain public dbt projects — this is a strong differentiator. Include a link to the generated dbt docs if you can host them, which allows recruiters to browse your data model documentation interactively.