Building dbt models faster with Google AI
đ Global friendly sessions on April 1 & 2
AI is only as useful as the context it has. For data engineering, that context lives in dbt. Your models, lineage, tests, business logic, and documentation all live in one structured, queryable layer. It's the missing grounding layer between Google AI and your data platform.
In this session, you'll see what happens when you connect them.
We'll show how Google's Gemini CLI and Antigravity IDE plug into your dbt project through dbt's MCP server, giving AI agents real visibility into how your data is built, tested, and documented. Instead of a generic coding assistant, you get one that understands your pipeline and can act on it.
We'll demo this live against real BigQuery data, showing how dbt agent skills let you delegate real work, including scaffolding models, writing data quality tests, diagnosing failures, and running lineage impact analysis, all without leaving your IDE.
If youâre exploring the future of AI-native data engineering, this session will give you a practical look at whatâs possible today.
Stephen Robb
Partner Solutions Architect
dbt Labs
Jobin George
Head of Global Data & AI Partner Engineering
Host Partner
