Data Quality Frameworks

Data Quality Frameworks

Expert in embedding quality validation into data pipelines as a dependency, not an afterthought.

When to Use This Skill

Use when:

• Building comprehensive data quality validation into pipelines
• Setting up Great Expectations suites and automated checkpoints
• Creating dbt test suites (schema tests, relationship tests, custom tests)
• Establishing data contracts between producer and consumer teams
• Monitoring data quality metrics, SLAs, and anomalies
• Debugging data quality failures or regressions
• Implementing layer-based validation (Bronze schema, Silver business rules, Gold aggregations)
• Blocking bad data from proceeding downstream

Core Capabilities

1. Great Expectations - Build expectation suites, checkpoints, automated validation
2. dbt Testing - Schema, relationship, and custom test strategies
3. Data Contracts - Producer/consumer agreements (ODCS, datacontract-cli)
4. Quality Monitoring - Continuous validation, metrics tracking, alerting
5. Debugging - Root-cause analysis of data quality anomalies
6. Layer-Based Validation - Schema (Bronze), rules (Silver), aggregation checks (Gold)

Framework References

For detailed implementation guidance, see:

Great Expectations

Use when: Building GE validation suites and checkpoints

Covers:

• Building comprehensive expectation suites
• Checkpoint configuration and automation
• Running validations and handling failures
• Integration patterns and alerting

dbt Testing

Use when: Creating dbt test suites

Covers:

• Schema tests (unique, not_null, accepted_values, relationships)
• Custom generic tests (reusable across models)
• Singular tests (specific business rules)
• Test coverage best practices

Data Contracts

Use when: Establishing producer/consumer agreements

Covers:

• Data contract specification format
• Schema definitions with PII classification
• Quality expectations and SLA definitions
• Contract versioning and evolution
• Validation against contracts

Automated Quality Pipeline

Use when: Orchestrating end-to-end quality validation

Covers:

• Building orchestrated quality pipelines
• Multi-table validation workflows
• Quality reporting and metrics
• Integration with Airflow/orchestrators
• Blocking pipelines on failures

Quick Decision Guide

Quality Strategy

Layer-Based Testing

• Bronze (Schema): Validate schema, data types, null constraints
• Silver (Business Rules): Test foreign keys, categorical values, ranges
• Gold (Aggregations): Verify aggregation logic, metric calculations

Test Pyramid

• Most tests: Single column validations (fast, focused)
• Fewer tests: Cross-table relationships (slower, broader)
• Blocking vs Warning: Block bad data; warn on minor issues

Best Practices

Do's:

• ✅ Test early - Validate source data before transformations
• ✅ Test incrementally - Add tests as you find issues
• ✅ Document expectations - Clear descriptions for each test
• ✅ Alert on failures - Integrate with monitoring
• ✅ Version contracts - Track schema changes

Don'ts:

• ❌ Don't test everything - Focus on critical columns
• ❌ Don't ignore warnings - They often precede failures
• ❌ Don't skip freshness - Stale data is bad data
• ❌ Don't hardcode thresholds - Use dynamic baselines
• ❌ Don't test in isolation - Test relationships too

Common Pitfalls & Fixes

Dependencies

• data-pipeline-engineer - For pipeline orchestration and debugging
• dbt-transformation-patterns - For dbt project integration
• ops-manager - For monitoring dashboards and alerting (out of scope for this skill)

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