Data Engineering Best Practices
A concise playbook for building data systems that are reliable, observable, and calm to maintain.
1. Design for clarity first
Choose the simplest architecture that can deliver the next 12 months of outcomes. Complexity scales faster than data volume.
2. Make observability a product
Instrument every critical hop. Logs, metrics, and lineage should tell the same story to every stakeholder.
3. Ship quality gates
Data quality checks are your first line of defense. Treat them as part of the pipeline, not a separate project.
4. Build for people
Document the why, not only the what. Invest in clean interfaces, simple runbooks, and useful error messages.
5. Keep feedback tight
Short loops beat perfect documentation. Encourage weekly retros and track incidents with intention.