data-pipeline

End-to-end ETL orchestration is automated through a structured, phase-based workflow that handles counting, transformation, verification, and monitoring. It assigns defined roles like data-quality-manager and monitoring-specialist to coordinate cross-role execution across workspace phases.

Monitoring data pipeline SLAs is handled by a dedicated monitoring-specialist role within the workflow. Data quality is ensured through a data-quality-manager role that performs verification and validation across the pipeline phases.

To set up a data pipeline project, create the `_workspace/` project root, place input data in `_workspace/00_input.md`, and begin the three-phase pipeline workflow. The Skill provides workspace scaffolding and reusable templates for the pipeline structure.

Yes, the pipeline orchestration framework applies to both ETL and ELT workflows. It automates the full pipeline lifecycle including transformation, verification, and monitoring regardless of the specific data integration pattern.

The data pipeline workflow defines five specialized roles: etl-architect, data-quality-manager, scheduler-engineer, monitoring-specialist, and pipeline-reviewer. These roles coordinate cross-role execution across the structured workspace phases.

No, this data pipeline orchestration tool has no external dependencies. It operates independently using its own internal workspace scaffolding and templates to manage the ETL workflow phases.