Senior Staff AI Data Engineer

Stellar Health is hiring a Senior Staff AI Data Engineer to lead the transformation of analytics and reporting using AI, and to evolve the underlying data architecture.

You'll own, drive, and contribute to two key components of our analytics strategy:

• AI for analytics and reporting, end-to-end: strategy, tool selection, administration, architecture, semantic and context layers, and governance.
• Data architecture, including leading the thinking on how it and our models need to evolve to support AI use cases, alongside dbt + Snowflake work.

Together, these efforts aim to compress time-to-insight and improve the quality of insights.

What You'll Own

AI for Analytics and Reporting

• Set the strategy for how AI reshapes analytics, reporting, and self-serve data access at Stellar, including where AI will go first, what it will replace, what it will unlock, and how we will sequence its implementation.
• Own tool selection, administration, and architecture, including Claude on Snowflake, MCP servers, agent workflows, IDE integrations, and ensure reliability and scalability as usage grows.
• Design evaluation and guardrail systems to catch regressions before they ship, ensuring confidence in agent output for high-stakes reporting.
• Define how analytics use cases are proposed, governed, and shipped, ensuring AI access to data is both impactful and controlled.
• Debug complex failures, including agent problems that appear as LLM issues but are actually rooted in data modeling, metadata, or system design.
• Build for unpredictable futures, including new agent patterns, tools, business questions, and regulatory constraints.
• Serve as the role model and mentor for AI-assisted analytics across analytics engineers, analysts, and other engineers. Identify what’s worth sharing externally and lead content and communication efforts.

Data Modeling

• Collaborate with the analytics engineering team to extend and refine dbt + Snowflake models, focusing on structure, grain, and documentation required for agent accuracy, not just human navigation.
• Inform standards for how models, metrics, tests, documentation, and lineage are structured, and drive team-wide adoption of these standards.
• Lead the semantic and context layers, including metric definitions, business context, and metadata that make dashboards and agents trustworthy, developed with analysts and engineers.

Target Experience

• 8+ years across data platform, ML platform, or developer tooling, including time as the senior technical voice for a major data/ML/AI surface.
• Production experience architecting LLM systems, including agents, evaluations, RAG, MCP, tool use, multi-step workflows, and understanding failure modes.
• Strong dbt + cloud warehouse (Snowflake preferred) expertise, with a focus on semantic layers and metadata.
• Track record of driving wide-team consensus on technical direction, not just shipping but ensuring adoption of patterns.
• Comfortable communicating externally through blog posts, talks, open-source contributions, and customer-facing thought leadership.
• Bonus: Experience with Claude Code / Agent SDK, healthcare/claims data, and prior 0→1 platform builds.

Profile

• Act as an architect-mentor, not a solo individual contributor. Your impact is measured by the capabilities of the entire analytics org, not just what you ship.
• Equally credible in strategy conversations about where AI takes analytics next and in developing new data models.
• Decisions have direct, long-term consequences for Stellar's ability to operate on its data.