Within the past few years, a new persona has arrived on the modern data team: the analytics engineer. On platforms that seek to enable the intuitive workflows of data-warehousing in the cloud data lake—powered by engines like Dremio, Spark, and Presto—the analytics engineering toolset, including dbt, is a natural fit. By writing all transformation logic in SQL, critical business rules are accessible to the greatest number of people; by templating that SQL with Jinja, storing it in version control, wrapping it with automated tests and documentation, and persisting valuable metadata, the analytics workflow gains the rigor of software engineering principles.