Senior Software Engineer, Context Layer

Starburst's Enterprise Context Layer turns scattered data into governed, versioned Data Products , business definitions, access policy, and lineage built in , so BI tools, applications, and AI agents all work from the same trusted answer.

As a Senior Software Engineer on this team, you'll build the backend services that harvest business context from where it already lives, govern it through a certification and versioning workflow, and serve it to both people and AI agents across the platform.

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.
• Bonus: experience with data catalogs, semantic layers, or metadata/governance systems (e.g., dbt, Collibra) is a plus, not required.

Benefits:

• Competitive pay
• Attractive stock grants
• Flexible paid time off
• More