InfoBrowse the open roles. Use filters to narrow by location or role type. If you don’t see a position that fits, send us your profile at general@plexis.ai — we’ll review it anyway.LocationAllRemoteUSACanadaPolandRomaniaSloveniaUkrainVietnamMalaysiaUSA / CanadaTypeAllContractFull-timeInternshipNew Grad ProgramIntern / New Gradopen roles5Senior Healthcare Data Integration EngineerEngineeringRemoteContractEarly Career AssociateProductUSA / CanadaIntern / New GradDevOps EngineerEngineeringRemoteContractSenior Frontend Engineer — Workflow BuilderEngineeringRemoteFull-timeSenior Platform Lead — Java/KotlinEngineeringRemoteFull-timeShow allSenior Healthcare Data Integration EngineerCopy linkCopied!Copy linkShareSenior Healthcare Data Integration EngineerWhat you’ll buildYou will build the healthcare data ingestion, normalization, mapping, and data quality layer that allows Plexis to work reliably across fragmented healthcare systems.Your work will turn messy source feeds, APIs, files, and healthcare data formats into clean, validated internal records and timelines that can power workflows, analytics, and future workflow intelligence.This is not a generic data engineering role. You will be dealing with real-world healthcare data: inconsistent schemas, missing fields, changing formats, duplicate identifiers, late-arriving data, and source systems that do not behave cleanly.ResponsibilitiesBuild and maintain connectors for healthcare data sources, including APIs, FHIR, HL7v2, C-CDA, SFTP/file extracts, and batch feeds.Implement ingestion pipelines for batch and event-driven data.Map source data into Plexis’ internal canonical model, using FHIR-aligned concepts where appropriate.Implement code normalization, timestamp alignment, source-system mapping, and data-type validation.Build patient identity linkage and crosswalk strategies across MRNs, source-system IDs, encounter IDs, and external identifiers.Build encounter, episode, and event timeline construction logic.Implement deduplication, replay handling, late-arriving data handling, and source-file idempotency.Build data quality checks, schema drift detection, lineage tracking, and reproducible pipeline runs.Create validation reports and operational visibility into pipeline failures, incomplete records, and source-system inconsistencies.Prepare clean, validated datasets that can support analytics, workflow intelligence, and future ML use cases.Work closely with backend/platform engineers to ensure ingested data can safely trigger governed workflows.Work with customer/integration stakeholders to understand source-system constraints and data availability.Requirements6–10+ years of experience in data engineering, integration engineering, or healthcare interoperability.Strong Python experience.Strong experience building batch and event-driven data pipelines.Experience working with messy real-world source data and inconsistent schemas.Strong understanding of data validation, lineage, replayability, and reproducibility.Experience with PostgreSQL.Experience with object storage patterns such as S3-compatible storage.Experience building reliable pipelines with tests, monitoring, alerting, and failure recovery.Strong debugging skills across files, APIs, schemas, timestamps, identifiers, and source-system behavior.Ability to work closely with backend engineers on data contracts and workflow-triggering requirements.Strongly PreferredHL7v2 experience.FHIR experience.C-CDA experience.OMOP or healthcare canonical data model experience.Experience with hospital IT environments.Experience with interface engines or healthcare integration platforms.Experience with identity matching, MRN crosswalks, encounter modeling, or patient timeline construction.Experience with Great Expectations, Soda, dbt tests, or similar data validation tools.Experience handling PHI-sensitive data and healthcare privacy constraints.Apply Now
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