data-pipeline-engineering

# Data Pipeline Engineering Skill ## 🔴 AI FIRST Quality Principle > **Apply the AI FIRST principle: never accept first-pass quality. Minimum 2 iterations. Read all output, improve every section. No shortcuts.** ## Purpose Expert knowledge in designing robust ETL (Extract, Transform, Load) pipelines for automated data processing, focusing on reliability, monitoring, and maintainability. ## Core Principles 1. **Idempotency** - Pipeline runs produce same results 2. **Observability** - Full visibility into pipeline health 3. **Error Recovery** - Graceful handling of failures 4. **Version Tracking** - Track all data changes 5. **Monitoring** - Real-time pipeline health checks ## Enforces - ETL workflow patterns (Extract → Transform → Load) - Automated scheduling (cron, GitHub Actions) - Data versioning and archival - Pipeline health monitoring - Error recovery strategies - Audit logging ## When to Use - Building automated data pipelines - Scheduling data fetching workflows - Implementing data versioning - Monitoring pipeline health - Designing error recovery ## References - [GitHub Actions](https://docs.github.com/en/actions) - [ETL Best Practices](https://en.wikipedia.org/wiki/Extract,_transform,_load) --- **Version**: 1.0 | **Last Updated**: 2026-02-06 | **Category**: Development & Operations