Nava Benefits

Data Engineer

• Own ingestion of external health and benefits data into Nava’s domain models
• Build pipelines that fail loudly on bad input, reconcile source counts to normalized tables, and surface issues early
• Unify employee eligibility and elections data under consistent mapping, validation, and testing practices
• Scale pipeline processes to approximately 40× the current data footprint
• Evaluate OLAP versus OLTP storage and event streaming versus nightly jobs
• Make member-ID, eligibility, and claims mapping and identity decisions logged and reviewable
• Build observability and automation tooling, including TypeScript web applications and AI-assisted matching
• Operate pipelines as production software with alerts, idempotent reruns, backfills, and secure handling of SSNs and health information
• Participate in interviews involving pipeline experience, bad inbound data, hands-on coding, design, and accomplishments

AI Engineer

• Build and harden the platform underneath Nava’s LLM-based products
• Make context management, agent state, sandboxed execution, and model routing reusable across AI use cases
• Design state management and deployment patterns that allow long-running agent tasks to survive deploys, restarts, and retries
• Enable agent tasks to be resumed, inspected, and replayed
• Package context into zero-trust execution environments across platforms
• Eject and retrieve new context as agents work
• Route models to appropriate jobs and orchestrate subagents
• Measure and optimize token usage and reduce cost per task as volume grows
• Reduce unplanned platform work interrupting AI product teams
• Document, test, trace, and evaluate shared platform patterns
• Enable new engineers to adopt shared patterns quickly and catch regressions before users notice