Keep your data PRs honest

• Protect data integrity
  Define checklists for schema changes, transformations, and critical business metrics. Make sure someone actually checked row counts, nulls, edge cases, and backfills before merging.
• Standardise how pipelines get shipped
  Create simple, repeatable steps for new jobs and models. That can include docs updated, alerts configured, rollbacks planned, and tests run. Same workflow, whether it is a tiny tweak or a new pipeline.
• Make reviews less "tribal knowledge"
  Turn what your senior data people remember to check into checklists everyone can follow. Fewer "oh yeah, I should've mentioned that" comments after the fact.
• Leave an audit trail without spreadsheets
  Every PR shows which data checks were required and who ticked them off. Useful for regulated environments, SOC or ISO evidence, or just proving you did sanity check that metric.

By adding Pull Checklist to your data and analytics repos, you get consistent PR reviews, fewer broken dashboards, and clearer ownership. You also avoid writing a single GitHub Action or maintaining a side project for "data quality scripts".