Coding for data systems presents unique challenges compared to traditional software development, primarily due to the unpredictability and complexity introduced by the data layer, which is often invisible during code writing. Unlike software, where code changes are predictable and rollbacks straightforward, data code changes can lead to complex issues due to dependencies that often remain unflagged by typical data frameworks. Simple changes, such as modifying a field's type or renaming a column, can have far-reaching impacts on downstream processes, similar to breaking changes in APIs. Data engineers must navigate these challenges while dealing with disparate tools and platforms, making it critical to understand and manage implied data contracts and dependencies. A robust combination of technology and processes, akin to CI/CD in software engineering, is necessary to handle these complexities in data engineering, ensuring changes are assessed for potential impacts before being merged. The goal is to create a seamless integration of technology and process that comprehensively manages dependencies and impacts to facilitate efficient and safe data engineering practices.