--- title: "Metric Semantic Layer: How Lyft Governs and Scales Key Data Definitions" url: https://daily.dev/posts/metric-semantic-layer-how-lyft-governs-and-scales-key-data-definitions-u9nxixf52 source_url: https://eng.lyft.com/metric-semantic-layer-how-lyft-governs-and-scales-key-data-definitions-56bee3643c29 type: article source: "Lyft Engineering" published: 2026-06-10T19:24:13.794Z updated: 2026-06-10T19:24:40.612Z tags: ["python", "big-data", "mcp"] reading_time: 8 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Metric Semantic Layer: How Lyft Governs and Scales Key Data Definitions **[Lyft Engineering](https://daily.dev/sources/lyfteng)** · 8 min read · 0 upvotes · 0 comments ## Summary Lyft built an internal Metric Semantic Layer (MSL) to solve the problem of inconsistent metric definitions across teams. Implemented as a Python package, MSL uses YAML configurations with Jinja-templated SQL to store canonical metric definitions. Governance is enforced through 'Golden Metrics' selection criteria (metrics with at least two distinct use cases) and a dual-ownership model requiring both a Business Owner and an Operational Owner for every metric. The system exposes metrics via Python APIs, integrates with Lyft's Amundsen data catalog, offers a self-service UI for SQL generation, and supports AI agents through an MCP (Model Context Protocol) integration enabling natural-language metric queries in tools like Claude and Cursor. Lyft is now exploring vendor-managed solutions to extend MSL further into BI tools and AI-driven analysis. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Engineering The Semantic Layer: Principles for Data at Scale](https://daily.dev/posts/engineering-the-semantic-layer-principles-for-data-at-scale-q4yvyubwd) · Mercari Engineering · 1 upvotes · 0 comments - [How Lyft Built an ML Platform That Serves Millions of Predictions Per Second](https://daily.dev/posts/how-lyft-built-an-ml-platform-that-serves-millions-of-predictions-per-second-v99yv1odt) · ByteByteGo · 53 upvotes · 1 comments - [Whoever Writes the Semantic Layer Wears the Crown](https://daily.dev/posts/whoever-writes-the-semantic-layer-wears-the-crown-cd9t5ch2z) · Infostrux · 0 upvotes · 0 comments - [Lyft Scales Global Localization Using AI and Human-in-the-Loop Review](https://daily.dev/posts/lyft-scales-global-localization-using-ai-and-human-in-the-loop-review-vl3ytcueb) · InfoQ · 0 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#big-data](https://daily.dev/tags/big-data), [#mcp](https://daily.dev/tags/mcp) [View this post on daily.dev](https://daily.dev/posts/metric-semantic-layer-how-lyft-governs-and-scales-key-data-definitions-u9nxixf52)