Data Engineering Best Practices: The Complete Checklist

Best practices documents are easy to write and hard to use. They list principles without context, advice without prioritization, and rules without explaining when to break them. This one is different. It's a practical, tool-agnostic checklist organized by the categories that matter most — with each item tied to a specific outcome. Use this as a recurring audit. Run through it quarterly. Any unchecked item is either a technical debt item or a conscious tradeoff. Know which is which. Pipeline Design [ ] Separate ingestion from transformation. Raw data lands unchanged. Business logic runs separately. This lets you replay raw data and isolate failures. [ ] Model pipelines as DAGs. Each stage has explicit inputs and outputs. Independent stages run in parallel. Failed stages retry alone. [ ] Make dependencies explicit. If pipeline B needs the output of pipeline A, declare that dependency in your orchestrator. Don't rely on timing assumptions. [ ] Use sensors or triggers for scheduling. Wait