The Data Engineering Lifecycle is a process used by data teams to author, evolve, and maintain data pipelines and the data assets that those pipelines produce. The lifecycle consists of four phases: Development, Verification, Deploying to Production, and Monitoring and Debugging. In the Development phase, code is written and tested to improve or expand data pipelines. In the Verification phase, problems are caught before they reach production through a mix of manual and automatic testing. During this phase, tools like continuous integration (CI) infrastructure and automated unit tests help ensure that changes are thoroughly tested before deployment. The Deploying to Production phase involves getting new code running in production and updating data assets to reflect the changes. Finally, in the Monitoring and Debugging phase, issues are discovered and addressed by fixing upstream data sources or re-executing pipelines. A healthy Data Engineering Lifecycle enables fast shipping and improvement of data pipelines without impacting production data quality or timeliness.