Description
What we’re building We’re building an agentic analytics platform: an AI data analyst that lets business users ask questions in plain English and get trustworthy answers directly from their live data warehouse. The architecture follows the modern analytics stack: • Modern cloud data warehouse • Governed semantic layer with certified metrics, entity relationships, and business glossary • LLM agent grounded in that semantic layer • Exposed through MCP (Model Context Protocol) This isn’t a demo or research project. The platform is already live with paying customers running production workloads. We’re looking for someone who has already solved this problem in production. Someone who understands the difference between generating SQL and producing numbers a CFO will actually trust. The problem you’ll own Generating SQL with an LLM is relatively easy. Building an AI analyst that returns the same correct, governed answer every time is the hard part. That requires: • A governed semantic layer with certified metric definitions so “Revenue,” “Occupancy,” or any business metric has exactly one meaning. • Context engineering for structured data, including curated catalogs, table and column documentation, retrieval context, and keeping irrelevant information out of the model. • Retrieval that actually works using embeddings and semantic search across schema metadata, business glossaries, and previously verified queries so the agent consistently selects the correct tables and business defini