Netflix faced challenges with scattered, inconsistently defined, and untrustworthy metrics, particularly within its experimentation platform. This complexity hindered data scientists and analytics engineers, making metric onboarding take weeks through the previous system. To address this, Netflix explored semantic layer solutions and chose DataJunction due to its robust SQL parsing and generation engine, along with its graph-based metadata storage. DataJunction originated at Facebook to address metric portability issues and was later open-sourced and expanded upon by Netflix engineers. A key architectural decision was to prioritize an API-first approach, focusing on a solid semantic layer engine and API with SQL input and output capabilities. The platform adopted multi-dimensional cubes as a first-class feature for familiar user interfaces and efficient data delivery. DataJunction's ability to link metric definitions back to the source simplifies troubleshooting and ad-hoc analysis. The platform's integration into Netflix's experimentation domain has streamlined metric and dimension definition and management. Future plans include unifying general analytics at Netflix by building stronger integrations with other tools. This will enable easier discovery, usage, and experimentation with metrics across the Netflix data stack.