We're hiring a Data Engineer to own the external pipes of data into Nava's domain models. Health and benefits data arrives in many shapes: eligibility (census) files from HR and payroll systems, carrier feeds, member IDs, enrollment elections and claims. Your job is to ingest it, normalize it to our canonical models, then map and validate it so it connects to everything else we know about an employer and their people.
What makes this opportunity unique: clean, connected data unlocks the rest of Nava. It lets our AI help an employee understand their benefits and choose the right plan for their family. It puts a member ID in someone's hand at the point of care. And it powers the data-driven conversation between employers and brokers at the heart of Nava: the best plan options at renewal, and audits that check whether carriers bill for the people the HR system actually has enrolled. Carriers get it wrong more often than you would think.
What you'll accomplish in the 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, and surface problems before a downstream team or client does.
- Unify the census platform: Our eligibility and elections data runs through processes built over five years by different teams. Bring them under one set of mapping, validation and testing practices.
- Scale to about 40× today's data footprint this year: Find the efficiencies in our pipelines and revisit the decisions underneath them (OLAP versus OLTP storage, event streaming versus nightly jobs) so the platform absorbs the volume without a matching rise in cost or run time.
- Make mapping and identity explainable: Member IDs, eligibility and claims associate to the right person through logged, reviewable decisions, so we catch wrong matches before they reach a member.
- Build the tooling around the pipes: Ship the observability and automation the team works in, including TypeScript web apps and AI where it helps, such as fuzzy matching of names and plans.
- Run it as production software: Alerts on failures and data-quality regressions, idempotent re-runs, routine backfills, and secure handling of SSNs and health information.
What you'll bring
We understand that your experience is more than just a list of requirements, so we encourage you to apply even if you don't meet all of the following bullet points.
- Hands-on ownership of a production data pipeline other teams depended on, ideally with messy external sources and downstream users who noticed when data was wrong. Building it, inheriting it and keeping it running, or owning one stage (ingestion, normalization, mapping or validation) all count.
- Python and SQL depth that holds up without an AI assistant and lets you steer one: joins, indexes, query plans, why a query is slow and whether a fix is real.
- An orchestrator in production (Dagster preferred; Airflow or Prefect are comparable) and Postgres or a comparable relational database on a cloud.
- Proof habits: reconciliation, data-quality gates, tests you would trust at 3 a.m.
- AI-assisted development as a daily workflow: you know what to delegate and when the agent is wrong.
- Care with sensitive data, and clear communication with people who do not write code.
Not required: Dagster specifically, Spark-scale data, a degree, or a set number of years. Hands-on dbt is a strong plus, not a gate.
Our stack
Dagster, dbt and Python on Postgres and AWS; TypeScript web apps; Airbyte, Parabola and Salesforce around them; Claude Code, Codex and Cursor for development. We are open to other tools where a use case calls for them.
What you'll get
- Real ownership on a small data team, where your decisions on mapping, storage and orchestration set the practice as we scale.
- Work that visibly unlocks Nava's AI features, member experience and broker conversations.
- A direct line to the engineers and leaders making product and platform decisions.
- A remote-first company with a mission to fix healthcare and the tooling to do the best work of your career.
How to apply
Apply below with your resume; we read every application. The process starts with a short intro call, followed by a few conversations with the team, including a hands-on data pipeline session.
