# data-qa

> When to use: - You need a data quality audit for pipelines or datasets.

- Author: Yannik Pitcan
- Repository: pitcany/ds-sherpa
- Version: 20260205120049
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-08
- Source: https://github.com/pitcany/ds-sherpa
- Web: https://mule.run/skillshub/@@pitcany/ds-sherpa~data-qa:20260205120049

---


---
name: data-qa
description: Data QA checklist for pipelines, schemas, and invariants.
---

When to use:
- You need a data quality audit for pipelines or datasets.

Inputs:
- Dataset, schema, and expected invariants.

Output:
- Schema/contract checks (types, required fields, invariants)
- Freshness/lag validation
- Null/duplicate/outlier checks
- Logging completeness vs source of truth
- Backfill and historical drift review
- Return: a table of checks (status, evidence) + top five risks + next fixes