Which tools are supported by this skill?

This skill provides specific implementation patterns for Great Expectations (Python), dbt (SQL/YAML), and Data Contracts using SodaCL.

Can I automate these checks in my CI/CD pipeline?

Yes, the skill includes patterns for running checkpoints and tests as part of automated deployment, ingestion, and orchestration workflows.

Does it support custom business logic validation?

Absolutely; it provides templates for generic dbt tests, singular SQL tests, and Great Expectations suites to enforce specific domain-specific business rules.

How are validation failures handled?

The skill covers implementation patterns for Slack notifications, automated logging, and raising pipeline exceptions to prevent corrupt data from reaching production tables.

Data Quality Frameworks

Name: Data Quality Frameworks
Author: Tahir-yamin

byTahir-yamin

•

Analytics & Monitoring

Implements robust data validation and quality checks using Great Expectations, dbt tests, and formalized data contracts to ensure reliable pipelines.

About

This skill provides production-ready patterns for establishing high-trust data environments by integrating industry-standard validation tools. It enables data engineers to implement comprehensive quality dimensions—such as completeness, uniqueness, and timeliness—while automating checks within CI/CD workflows. By establishing clear data contracts between producers and consumers, this skill helps prevent broken downstream analytics and maintains data integrity across complex modern data stacks.

Key Features

Great Expectations suite and checkpoint configuration patterns
Data contract definitions using SodaCL and YAML specifications
3 GitHub stars
Automated dbt data and schema testing implementation
Multi-dimensional validation covering completeness, validity, and consistency
Automated quality pipeline orchestration with Slack alerting

Use Cases

Building automated data validation steps in production ETL/ELT pipelines
Monitoring production data health with recency, statistical checks, and schema validation
Enforcing data contracts between upstream engineering and downstream analytics teams

About

Key Features

Great Expectations suite and checkpoint configuration patterns
Data contract definitions using SodaCL and YAML specifications
3 GitHub stars
Automated dbt data and schema testing implementation
Multi-dimensional validation covering completeness, validity, and consistency
Automated quality pipeline orchestration with Slack alerting

Use Cases

Building automated data validation steps in production ETL/ELT pipelines
Monitoring production data health with recency, statistical checks, and schema validation
Enforcing data contracts between upstream engineering and downstream analytics teams