How DoorDash scaled merchant-facing analytics to thousands of users in weeks, on dbt and ThoughtSpot
Blog post from dbt
DoorDash scaled governed analytics by separating its semantic layer from its consumption tools, using dbt to define shared metrics across Iceberg tables and Snowflake while allowing platforms such as ThoughtSpot to consume those definitions without creating duplicate semantic models. Following acquisitions of Deliveroo and Wolt, the company faced the challenge of consolidating multiple existing semantic layers while preserving speed for individual teams. The architecture supports tightly controlled internal finance and revenue reporting, including compliance and audit requirements, as well as fully white-labeled embedded analytics for merchants with role-based data access. DoorDash reported reaching thousands of external users within weeks for a new use case. Its leaders emphasize treating semantic models as version-controlled, reviewed code and making governed data access as convenient as ad hoc querying. The same dbt-defined semantic layer also supports DoorDash’s AI efforts, which have evolved from domain-specific agents to a global agent covering roughly 70 to 80 percent of its data domains.
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