Data Quality Test Generator

About

This skill streamlines the implementation of robust data quality gates by providing standardized templates and guidance for dbt 1.10+ environments. It automates the creation of complex test patterns for Kimball-style data architectures, covering everything from grain uniqueness and foreign key relationships in fact tables to SCD Type 2 validation in dimension tables. By enforcing specific syntax requirements—such as moving from 'tests' to 'data_tests'—and providing domain-specific validation logic, it helps analytics engineers maintain high-quality, trustworthy data pipelines while reducing the manual effort of writing boilerplate YAML configurations.

Key Features

• Automates source freshness monitoring and categorical enum validation.
• Provides specialized validation patterns for Staging, Fact, Dimension, and Mart models.
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• Generates dbt 1.10+ compliant syntax using data_tests and argument blocks.
• Enforces Kimball design patterns including grain uniqueness and FK relationship tests.
• Supports advanced business logic checks including row count thresholds and aggregation consistency.

Use Cases

• Migrating legacy dbt test configurations to modern 1.10+ syntax requirements.
• Validating SCD Type 2 history and date range logic in player or entity dimensions.
• Establishing comprehensive data quality gates for new fact and dimension tables.