Note: The job is a remote job and is open to candidates in USA. Harvey Nash is seeking a Senior Data Engineer to own pipelines that handle protected health information and the broader data ingestion system. The role involves designing, building, operating, and automating healthcare data pipelines, ensuring reliable processing, observability, governance, and incident response.
Responsibilities
- The primary responsibility for this position is to own the pipelines which touch protected health information (PHI). These are written in Python, using Ibis / DuckDB / Pandas, and run on Dagster as well as Kubernetes
- You will be the design owner for our PHI pipeline portfolio and for our data ingestion system, working within an architecture the CTO sets (lake layout, PHI segregation, orchestration patterns). You are being hired to make good engineering decisions inside a well-defined boundary, explain them clearly, get them reviewed, and ship them with strong and creative use of the Codex AI coding assistant
- You will also be the engineer of last resort for these pipelines. When a PHI pipeline fails at a moment that matters to a client, you are the person who answers
- Design, build, and operate the pipelines that handle identified patient data in Dagster on our DuckLake/Ibis/DuckDB lakehouse. This includes the patient-identity and record-linkage logic that determines whether a client’s data lines up correctly
- Own the ingestion lifecycle end to end: client file submission through preprocessing, validation, enrichment, masking, and audit. Ingestion is event-driven on Azure Event Grid and Service Bus, and your job is to make it more stable, more observable, and more automated than it is today
- Our ingestion recovery and data movement are still partly manual. Turn them into fully idempotent, re-runnable, record-level-retryable processes with enterprise-grade observability and governance
Skills
- Healthcare data literacy (required). You have worked with patient-level data under HIPAA, and you know what an MRN, a registry, a quality measure, a benchmark, and a BAA are without being told
- Expert Python in production data systems
- Production SQL — you write it, tune it, and read query plans
- Deep experience with one modern orchestrator (Dagster, Air?ow, Prefect, or similar) and the judgment to explain why it was the right choice and where it wasn't
- Ingestion lifecycle experience — ?le intake, validation, event-driven or queue-driven processing, failure handling and recovery, audit
- Kubernetes — you have deployed and operated workloads on it. You don't need to run the cluster but you know the di?erence between a node, an image, and a pod
- Observability instincts — you build things so that when they fail, you know, and you know why
- AI-assisted development practice with the discipline to own the output
- Sound engineering reasoning across the board — you can explain the tradeo?s behind every design decision you've made
- US-based, authorized to work in the US without sponsorship
- Background check required
- Dagster is a big plus
- Eastern or Central time zone preferred
- Strongly preferred (one or more) Clinical registry or quality-reporting experience: NCDR, STS, GWTG harvest data, risk models
- Physician or service-line performance reporting
- EMR/EHR integration: Epic, Cerner, Allscripts, HL7 v2/FHIR feeds at multi-hospital scale
- Azure. DuckDB / Ibis. MySQL. Helm/Terraform
- Patient identity resolution / record linkage — if you've built it, we want to hear about it
- On our stack: Azure, Dagster, DuckLake/DuckDB/Ibis, Event Grid and Service Bus, MySQL, AKS
- We don't expect you to know this exact combination. We do expect you to be the kind of engineer who has learned a new orchestrator and a new cloud before, and can tell us how it went
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