Engineering
Backend / Data Engineer
Own the data layer. EHR ingestion, normalization, and the pipelines that feed the trajectory engine.
El puesto
Clinical reasoning is only as good as the data feeding it. You will own the data layer end-to-end: ingestion from EHR systems, normalization across terminologies, time alignment across signals, and the pipelines that hand clean longitudinal patient records to the trajectory engine.
Today the engine runs against research databases. The next phase is real hospital data: Epic, Cerner, Meditech, with their format quirks, missing fields, and inconsistent coding. You will design the layer that absorbs that reality without bleeding it into the engine.
Qué hará
- Designing and building EHR ingestion pipelines for hospital pilot sites
- Normalizing clinical data across terminologies (ICD-10, SNOMED CT, LOINC, RxNorm) and across vendor formats (FHIR, HL7, OMOP)
- Handling time alignment, deduplication, and longitudinal merging across multiple data sources per patient
- Building data quality checks, monitoring, and clinical reasonableness validation
- Partnering with hospital IT teams on technical integration
- Designing the data contracts the trajectory engine depends on
Su primer año
In your first 12 months: production-grade EHR ingestion running for the first hospital pilot site, normalized OMOP-style data layer in place, automated data quality monitoring, and clear documentation other engineers can build against.
Qué aporta
- Strong Python and SQL
- Four-plus years building data pipelines at scale
- Experience with healthcare data formats (FHIR, HL7, OMOP, or similar) or equivalent regulated-data work
- Comfort with data quality challenges: missingness, deduplication, multi-source reconciliation
- Working knowledge of relational databases; PostgreSQL preferred
- Ability to work directly with clinical and engineering stakeholders
Deseable
- EHR integration experience (Epic, Cerner, Meditech)
- Working knowledge of clinical terminologies
- Streaming or event-driven data pipelines (Kafka, Pub/Sub)
- DBT, Dagster, or similar transformation/orchestration frameworks
- Prior experience with HIPAA-regulated production environments
Healthcare data experience is preferred but not required. Strong general data engineering skill plus willingness to learn the clinical data world is enough.
Sobre Chroneme
Chroneme is building neuro-symbolic AI for medicine: one record of each person, in order and in context, read by learned models and decided by explicit clinical rules that state their reason and their source. It tells the care team who needs attention and why, and prepares the work for a clinician to sign.
We are a small team in Birmingham, Michigan, building with clinicians and validating under a pre-registered protocol. You will work directly with the founders, with no layers between you and the work. Early team members get meaningful equity.
Cómo contratamos
- 1An introductory call to discuss your background and interest (30 minutes, with a cofounder)
- 2A technical conversation walking through a pipeline you built, followed by a discussion of the data integration challenges in front of us. (60 minutes, with the founding team)
Postularse
Un currículum y unas líneas sobre por qué este puesto. Tell us about a data pipeline you built that worked under real conditions. What was the hardest part? También puede escribir a hiring@chroneme.com.
Chroneme es un empleador que ofrece igualdad de oportunidades. Este puesto requiere autorización para trabajar en Estados Unidos; por ahora no podemos patrocinar visados.