About the job
DoorDash is building the world's most reliable on-demand, logistics engine for delivery! We're looking for talented engineers to help us develop a 24x7, global infrastructure system that powers DoorDash's three-sided marketplace of consumers, merchants, and dashers. Data Platform’s Data User Experience Engineering builds the products every DoorDash employee uses to answer questions with data. That portfolio includes our AI first reporting and authoring surface, Conversational analytics with AI, Semantic and Knowledge layer along with MCP and skills layer plus the full stack infrastructure that lets users and agents reach all of it. You will own the full path from a question to an answer an operator can put in a deck without checking it twice.
Responsibilities
Ship agentic analytics features to thousands of users where accuracy is the product
Build a data platform for authoring, dashboard runtime, drill-down and cross-filter semantics, scheduling and alerting, content certification and permissions, and the embedding path
Stream large result sets to the browser, rendering dozens of interdependent tiles
Build the AI experience inside the platform including agent-assisted query authoring and generated charts held to governance standards
Evolve the semantic layer metric and dimension modeling, definition governance, lineage and expose metadata in forms a model can reason over
Own the query and adaptive caching path including planning and federation across different query engines and invalidation correctness
Develop AI-enabled services and workflows, including self-serve tooling for creating, evaluating, and improving AI assistants
Build and operate data discovery and metadata platforms that help users find the right datasets, dashboards, and metrics quickly
Qualifications
Minimum
B.S., M.S., or PhD. in Computer Science or equivalent
3+ years of industry experience in software engineering
Strong backend fundamentals, especially in Go, Python and the ability to own a service in production
Shipped an LLM-powered feature to real users, hands-on with retrieval and grounding and built or meaningfully extended an eval system
Built meaningful pieces of data platform products- semantic layers, data discovery, reporting applications, caching systems and data applications
Familiarity with a cloud-based environment such as AWS and with Kubernetes in production
Experience with MCP, agent frameworks, or tool-calling architectures in production
Experience with traditional and modern reporting or analytics tooling, data governance, controls and multi-tenant data platforms
Prompt and context optimization at scale - caching, token budgeting, cost/latency tuning
Preferred
Full stack experience is a plus