Workflow Data Pipeline

Lifecycle for building data pipelines, analytics engineering, and data quality checks.

This is a Workflow skill. When executing this lifecycle, you should progress through the phases sequentially. For each phase, invoke or refer to the specific sub-skills that are contextually relevant to the project's language or framework.

Lifecycle Phases

1. Storage & Ingestion

• postgresql
• api-design-principles
• async-python-patterns

2. Analytics Engineering & Transformation

• dbt-transformation-patterns
• pandas-specialist
• numpy-specialist

3. Quality & Contracts

• data-quality-frameworks
• test-driven-development

4. Visualization & Storytelling

• data-storytelling
• grafana-dashboards