This article presents an ETL design document template used at Square to improve data quality, team consistency, and documentation practices.
•Templates reduce cognitive load by setting consistent expectations for peers, stakeholders, and consumers across data science teams
•An ETL design doc aligns consumers on technical decisions and serves as living documentation, preventing institutional knowledge loss when team members leave
•The Goals and Key Analytical Questions sections help clarify whether an ETL is truly necessary and ensure it models data appropriately for consumer needs
•Design docs enable peer review before code is written, allowing architectural feedback and iteration prior to building, saving time on revisions
•A Data Quality Checks section ensures query logic is tested and helps prevent future bugs stemming from untested work
