dbt Analytics Engineering

About

This skill empowers Claude to act as a seasoned analytics engineer, facilitating the development, modification, and debugging of dbt models with a focus on modularity and DRY principles. It provides a comprehensive framework for building scalable data pipelines, including automated data discovery, rigorous testing, and impact evaluation of changes. By integrating dbt-specific reference guides and best practices, it ensures that your analytics code is efficient, well-documented, and cost-effective while managing warehouse resources intelligently.

Key Features

• Integrated data validation and profiling using dbt show
• Impact assessment for downstream model dependencies
• Streamlined debugging for parsing, compilation, and SQL errors
• Automated dbt model construction and logic refactoring
• 13 GitHub stars
• Cost-aware development using deferral and zero-copy clones

Use Cases

• Refactoring legacy SQL into documented, tested dbt models
• Onboarding to and exploring unfamiliar data warehouse schemas
• Building and scaling modular data transformation pipelines