des-persona-data-quality-engineer

What problem does it solve?

It helps teams define data quality in a way that is measurable, monitorable, and actionable during a data engineering lifecycle rather than leaving quality as vague documentation.

Core Features & Use Cases

• Layer-aware data quality design: Selects DQ dimensions per layer (e.g., Bronze/Silver/Gold) with thresholds that reflect different baselines and expectations.
• Operational orchestration & observability: Specifies how quality checks run, how failures are detected and surfaced, and what operational responses occur (alerts, retries, runbooks).
• Governance-ready handling: Adds handoffs when DQ rules involve PII/retention concerns or when validation implies architecture/design changes.
• Use Case: When onboarding a new ingestion pipeline, define completeness and validity thresholds with baseline windows, set retry/backoff behavior, and require an owner-backed remediation path before promoting to downstream layers.

Quick Start

Use the skill to define your data quality dimensions, thresholds with baseline windows, and the alerting and runbook response rules for a pipeline going from Bronze to Gold.