What problem does it solve? Building and maintaining dbt pipelines for BigQuery requires deep knowledge of dbt project structure, profiles configuration, SQL optimization, and BigQuery-specific quirks like BigLake Iceberg naming. This Skill guides an AI agent through the full lifecycle of creating, modifying, troubleshooting, and validating dbt projects so models compile correctly and follow best practices. ## Core Features & Use Cases - dbt Project Scaffolding & Configuration: Initializes new dbt projects in a dedicated folder with correct dbt_project.yml and profiles.yml settings mapped to BigQuery project, dataset, and location. - SQL Optimization & Data Cleaning: Automatically rewrites inefficient patterns (e.g., IN (SELECT ...) to EXISTS) and applies mandatory data cleaning protocols, with summary sections in every response. - Troubleshooting & Validation: Diagnoses local and remote (Cloud Composer) DAG failures via gcloud logging, compiles with dbt compile, and never runs dbt run without explicit user confirmation. - Use Case: Ask the agent to create a new dbt model that transforms a raw BigQuery table into a cleaned, documented, unit-tested staging model — it will fetch schemas, generate the model with a config block, add tests, and compile to verify. ## Quick Start Ask the agent to create a new dbt model in BigQuery that aggregates daily sales from your source table, and it will scaffold, optimize, and compile the pipeline for you.
