What problem does it solve? Building and maintaining dbt pipelines for BigQuery requires deep knowledge of adapter quirks, SQL optimization patterns, and project configuration. This Skill guides an AI agent through the full lifecycle of creating, modifying, validating, and troubleshooting dbt models so pipelines compile correctly and follow BigQuery best practices. ## Core Features & Use Cases - dbt Project Scaffolding: Initializes new dbt projects with correct dbt_project.yml and profiles.yml configuration mapped to BigQuery project, dataset, and location settings. - SQL Optimization: Automatically rewrites inefficient patterns (e.g., IN (SELECT ...) to EXISTS) and proposes conditional optimizations like UNION ALL or APPROX_COUNT_DISTINCT with user confirmation. - Validation & Troubleshooting: Compiles models with dbt compile, runs dbt test, diagnoses remote orchestration failures via gcloud logging, and resolves missing-profile errors. - Use Case: A data engineer asks the agent to add a new staging model reading from a BigLake Iceberg source. The Skill inspects schemas with bq CLI commands, generates the model with proper source() references and config blocks, adds unit tests, and compiles the project to verify correctness. ## Quick Start Ask the agent to create a new dbt model in your project that transforms a specific BigQuery source table and compile the project to validate it.
