AI Engineer — Gtm Analytics

SLSQ327R637 At Databricks, we are passionate about enabling GTM Analytics Engineering builds the internal data and AI platform that Databricks' own go-to-market organization runs on — used by 6,000+ field users across sales, strategy, and operations. We own the number one Databricks App, the Genie agents, which are powered through the lakehouse (Unity Catalog bronze/silver/gold) beneath them. We are "Customer Zero" for Databricks: we adopt the newest Databricks and AI tooling first, in production, and pave the path for the rest of the company. We're in the middle of a step-change — moving the app from AI/BI and Genie experience to an AI-native, genie-centric platform that delivers prescriptive recommendations (not just descriptive reports), built to scale for the next 3–5 years. We build with AI in the loop end to end, and a large and growing share of our code is AI-generated and human-reviewed. The role As an AI Engineer you'll help build the intelligence and application layers of that platform: Genie-powered analytics agents, LLM-driven workflows (feedback analysis, "what's new" enablement, BRD-to-plan automation), and the Databricks Apps and Lakebase services that serve them. You'll work alongside senior engineers, and you'll be expected to lean on AI coding tools heavily and thoughtfully from day one. This is a high-ownership, fast-shipping team where early-career engineers get real surface area. What you'll do Build and ship features for AI-powered surfaces: Genie analytics agents, LLM-assisted feedback and enablement workflows, and in-app intelligence inside the GTM Hub. Develop and integrate agentic workflows — prompts, tools/MCP integrations, retrieval, and evaluation — that turn business questions into reliable, grounded answers. Write Python and SQL against our Databricks lakehouse (Unity Catalog, Delta) and Lakebase (Postgres) services that back the apps. Use AI coding tools (Claude Code, agentic skills) as a core part of your workflow, and help build the reusable skills and harnesses that make the whole team faster. Contribute to evaluation and quality: help measure and improve the accuracy of our AI outputs (eval sets, scorecards, regression checks). <li style="font-family: helvetica, arial, sans-serif;&quot