Phare Health

Software Engineer, Data Ingestion, Customer Integration

Phare Health

full-time

Posted on:

Origin:  • 🇺🇸 United States • New York

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Salary

💰 $150,000 - $220,000 per year

Job Level

Mid-LevelSenior

Tech Stack

AirflowApacheAWSCloudGoogle Cloud PlatformPython

About the role

  • Phare Health: reimagine healthcare payments by making reimbursement transparent and fair; focus on complex AI R&D
  • Own data ingestion to implement Phare’s platform at partner provider and payer systems; integrate via HL7, X12, SFTP, and API pipelines
  • Hands-on role at intersection of healthcare data, systems integration, and architecture, bridging AI product with real-world health data infrastructure
  • Design and implement data pipelines; set up validation and quality checks; monitor pipelines and ensure reliable data availability
  • Resolve data fidelity, transformation, and mapping challenges; collaborate with engineering, product, and data science teams; document architectures and build scalable playbooks; ensure HIPAA compliance

Requirements

  • Problem Solver: Able to debug messy healthcare data and build solutions that are robust, scalable, and compliant
  • Systems Thinker: Experience architecting or scaling integrations that balance reliability, performance, and security
  • Collaborator: Skilled at working with customer IT teams, clinical/operations stakeholders, and internal product/engineering peers
  • Startup Mindset: Comfortable in a fast-moving environment, eager to take ownership and shape integration processes as we scale
  • Experience with cloud-based integration platforms (AWS/GCP, Mirth, Redox, etc.)
  • Familiarity with cloud-based data workflows and scheduling/orchestration frameworks (e.g. Apache Airflow) is a plus
  • Familiarity with healthcare claims data and reimbursement workflows (e.g., in EHRs, claims, payer systems, or healthcare APIs)
  • Hands-on experience with HL7, X12, FHIR, SFTP pipelines, and healthcare interoperability standards
  • Exposure to data validation, analytics, or ML-driven use cases