[Hiring] Data Engineer @Nava Benefits

Role Description

We’re hiring a Data Engineer to own the external pipes of data into Nava’s domain models. Your job is to ingest it, normalize it to our canonical models, then map and validate it so it can be associated with everything else we know about an employer and their people. We are a concentrated data team whose job is the hygiene, quality and leverageability of external data inside the Nava ecosystem.

What makes this opportunity unique:

• Clean, connected data unlocks the rest of Nava.
• It helps employees understand their benefits and utilization.
• It drives data-driven conversations between employers and brokers.

What You’ll Accomplish in Your First Year

• Own ingestion end to end:
• Every external source lands through pipelines that fail loudly on bad input.
• Reconcile counts from source to normalized tables.
• Surface problems before a downstream team or a client does.
• Unify the census platform:
• Bring employee eligibility and elections data under one set of mapping, validation and testing practices.
• Prove each change matches before it ships.
• Scale it to about 40× today’s data footprint this year:
• Find efficiency in pipeline processes.
• Revisit decisions including OLAP versus OLTP storage.
• Absorb volume without a proportional increase in cost or run time.
• Make mapping and identity explainable:
• Ensure member IDs, eligibility and claims associate to the right person.
• Catch wrong associations before they reach a member.
• Build the tooling around the pipes:
• Ship observability and automation, including TypeScript web applications.
• Utilize AI for softer matching of names and plans.
• Run it as production software:
• Implement alerts on failure and data-quality regressions.
• Handle secure SSNs and health information in every file.

Qualifications

• Hands-on ownership of a production data pipeline.
• Python and SQL depth without an AI assistant.
• Experience with an orchestrator in production (Dagster preferred).
• Proof habits: reconciliation, data-quality gates, tests.
• AI-assisted development as a daily workflow.
• Care with sensitive data and clear communication.

Requirements

• Not required: Dagster specifically, Spark-scale data, a degree, or a years-of-experience number.
• Hands-on dbt is a strong plus, not a gate.

Benefits

• Real ownership on a concentrated data team.
• Work that visibly unlocks Nava’s AI features and member experience.
• A direct line to engineers and leaders making product decisions.
• A remote-first company with a mission to fix healthcare.

How We Interview

• A 15-minute introduction with our recruiter.
• A 15-minute AI conversation about a production pipeline you ran.
• Two interview days booked together:
• Day 1: 60-minute hands-on session in a small real data pipeline.
• Day 2: 60-minute design conversation and a 60-minute accomplishment discussion.

Working at Nava

As a remote-first company, Nava is committed to building a dynamic and inclusive culture where you have the autonomy to thrive. You’ll be supported by cutting-edge technology, a collaborative team, and a shared mission to revolutionize healthcare.

We encourage candidates from all backgrounds to apply, believing that diversity is essential for solving America's healthcare problem.