tomevault-io Data Pipeline Engineering Skill
Purpose
Expert knowledge in designing robust ETL (Extract, Transform, Load) pipelines for automated data processing, focusing on reliability, monitoring, and maintainability.
Core Principles
- Idempotency - Pipeline runs produce same results
- Observability - Full visibility into pipeline health
- Error Recovery - Graceful handling of failures
- Version Tracking - Track all data changes
- 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
Version: 1.0 | Last Updated: 2026-02-06 | Category: Development & Operations
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