back_office_ops · workflow
Metric Semantic Layer: How Lyft Governs and Scales Key Data Definitions
As Lyft scaled, different teams were using different definitions for the same metrics with no centralized version control or shared standard, causing outdated metric definitions to creep into decision-making.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Metric defined in YAML and SQL
Metric definitions and SQL logic are stored as YAML configurations with SQL Jinja templates in the MSL Python package.
Tools used
YAMLJinjaPythonAirflowAmundsenMCPClaudeCursorHex
Outcome
Lyft's MSL provides a single source of truth for metric definitions, and the AI MCP layer enables natural-language queries answered with greater accuracy and fewer hallucinations, integrating with Claude, Cursor, and Hex.
Results
Volumegreater accuracy and fewer hallucinations
Grounding & classification
Source type: technical build writeup
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