How Sigma's Data Platform Team Manages the Semantic Layer with dbt and Dagster

Sigma’s data platform team manages its semantic layer as code using dbt and deploys it through Dagster alongside warehouse-table changes, aiming to ensure that dashboards, analysts, and AI systems use consistent definitions for metrics such as ARR. The approach addresses governance, lineage, version control, reviewability, and AI reliability by storing business concepts, relationships, metrics, access restrictions, synonyms, and SQL guidance in domain-specific semantic models rather than broad, manually maintained interfaces. Sigma organizes these objects in a dedicated semantic dbt layer and uses open-source packages to represent Sigma data models, Snowflake Semantic Views, and Cortex Agents as nodes in the dbt dependency graph. Dagster then runs incremental dbt builds after merges, syncs changed warehouse connections and data-model definitions through Sigma’s API, and stores updated manifests, allowing tables and semantic models to be deployed together. These governed models support Sigma Agents, Sigma Assistant, Snowflake Cortex Agents, search services, and coding agents, enabling natural-language queries and automated workflows to trace answers back to shared business definitions.

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