Senior Software Engineer, Context Layer

Starburst delivers secure, governed access to enterprise data for AI, analytics, and enterprise intelligence. The Senior Software Engineer will build and operate Java-based backend services for the Enterprise Context Layer, including context harvesting, certification and versioning workflows, APIs, and integrations for AI agents and BI tools. The role also involves cross-functional product collaboration, reliability ownership, and occasional full-stack work.

Responsibilities:

• Design, build, and operate backend services that collect and reconcile business context from a variety of existing systems into governed Context Studio
• Build and evolve the certification and versioning workflow that lets teams trust a single definition of their metrics and data
• Contribute to the APIs and MCP-based integrations that let AI agents and BI tools query certified context directly
• Own reliability and data consistency for services that merge and reconcile information from many sources at once
• Partner directly with product on what gets built, not just how — and pick up frontend, tooling, or other full-stack work when it's the fastest way to unblock a feature or validate an idea
• Work cross-functionally with teams across the platform, including those building AI-powered features, to ship context-aware capabilities
• Provide considerate, timely review of your peers' pull requests and design proposals

Requirements:

• A builder's mindset with strong product thinking — you care about the "why" behind a feature, not just the elegance of the implementation
• Extensive software development experience with Java, with the versatility and willingness to work across the full stack — frontend, tooling, infra — when the problem calls for it
• Experience designing, building, and operating distributed backend services in production
• Experience designing and operating REST APIs
• Experience modeling and serving graph-structured or richly-relational data
• Experience integrating LLMs into backend systems — structured extraction, prompt-based enrichment, or evaluating LLM output for correctness — relevant to how we help harvest and enrich context
• Comfort working in ambiguous, fast-evolving problem spaces where architecture is still being defined
• Hands-on experience using agentic coding tools in real production work, with the judgment to apply proper guardrails — testing, review, verification steps — so speed doesn't come at the cost of code quality or correctness
• Demonstrated experience with software engineering best practices
• experience with data catalogs, semantic layers, or metadata/governance systems (e.g., dbt, Collibra)