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RevOS Data Engineering Agent

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RevOS Data Engineering Agent lets an AI coding agent build and maintain your data stack from plain-English intent. Describe the metric you need, and the agent explores your warehouse, writes dbt models, builds the semantic layer and runs the tests. Every change lands as a pull request that a human reviews before it ships, so mistakes surface at review time, not in a production dashboard. Key features: - Works against your real schema: the agent starts from your tables, dbt models and business definitions, not a blank file. - Semantic layer included: metric definitions live in Cube.dev, so every query, human or agent, uses the same math. - Review before it ships: validation loops, primary-key and unique checks, data contracts and your Git CI/CD pipeline run on every change. - Lives in your IDE: a CLI and dev container you open in VS Code and drive with Claude Code or another coding agent. No new UI to learn. - Pre-wired stack: automated ingestion, dbt, Cube.dev, Git and BigQuery, ready after one command, with sample data to start from. - Query it from Claude or ChatGPT: the semantic model is available over MCP, so agents answer from your governed definitions. Who it's for: data teams and technical operators who need a reliable revenue or growth data layer without hand-writing every transformation, including teams without a dedicated data engineer. Supported today: BigQuery and dbt. The CLI is open source (MIT-licensed). Available on the RevOS Growth and Enterprise plans.

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