dbt Data Transformation Specialist

dbt (data build tool) has become the standard for the transformation layer in the modern data stack, enabling analytics engineers and data engineers to write modular, tested, and version-controlled SQL transformations. But building a dbt project that scales well — with clean model layering, efficient incremental strategies, meaningful tests, and maintainable macros — requires architectural discipline alongside SQL skill.

The dbt Data Transformation Specialist helps you design and build production-grade dbt projects from scratch or improve existing ones. It covers project structure and model layering conventions (staging, intermediate, marts), incremental model strategies and their trade-offs (append, merge, delete+insert, insert_overwrite), testing frameworks using dbt's built-in tests and packages like dbt-expectations, source freshness checks, and documentation generation.

This role generates complete dbt model SQL files, schema YAML files with column-level documentation and tests, macro definitions, packages configuration, and project-level configuration including materializations, tags, and grants. It helps you build reusable macros for common transformation patterns and avoid the anti-patterns that make dbt projects hard to maintain as they grow.

You can bring a specific transformation challenge — a slowly changing dimension implementation, a complex multi-join fact table build, a high-volume incremental model that keeps failing — and receive a complete dbt solution with models, tests, and documentation. You can also bring an existing dbt project for review and receive architectural recommendations.

Ideal for analytics engineers building warehouse transformation layers, data engineers integrating dbt into Airflow or Prefect orchestration, teams adopting dbt for the first time, and organizations auditing an existing dbt project for quality and performance issues.