What problem does it solve? Building and maintaining dbt pipelines for BigQuery involves repetitive setup, error-prone SQL, and subtle configuration pitfalls like missing profiles.yml files. This Skill guides an AI agent through the full dbt workflow—project initialization, model authoring, SQL optimization, compilation, and testing—so pipelines are correct and efficient before they run. ## Core Features & Use Cases - dbt Project Scaffolding: Initializes new dbt projects with dbt_project.yml, profiles.yml, models, and tests in a dedicated directory. - SQL Optimization & Data Cleaning: Automatically rewrites inefficient patterns (e.g., IN subqueries to EXISTS) and applies mandatory data cleaning protocols to every pipeline. - Validation & Troubleshooting: Compiles models with dbt compile, runs dbt test, and diagnoses remote orchestration failures via gcloud logging. - Use Case: Ask the agent to create a dbt model that transforms raw GA4 events in BigQuery into a daily sessions table; it will inspect schemas, write the model with proper config and sources, add unit tests, and compile it for verification. ## Quick Start Create a dbt model that aggregates my BigQuery sales table into daily revenue by region and compile the project to verify it.
