8–12 years of experience in data engineering or backend development, with at least 5 years focused on healthcare data
Deep understanding of FHIR (Fast Healthcare Interoperability Resources)
Proficiency in Python, particularly for building and managing Airflow DAGs and programmatically triggering workflows
Strong knowledge of EHR systems (Epic Clarity, Cerner)
Requirements:
Design, build, and maintain data pipelines using Apache Airflow and Python to process and manage healthcare data
Programmatically trigger and manage Airflow DAGs using Python, ensuring dynamic and reliable pipeline orchestration
Ingest, normalize, and transform data from EHR systems (Epic Clarity, HL7, FHIR resources)
Work extensively with SMART on FHIR APIs for clinical data integration
Job description
Job Title _ Data Engineer
Location _ Remote
Duration _ Contract
Job Description
We are seeking an experienced Senior Data Engineer with a strong background in healthcare data systems to join our team. This role demands a deep understanding of clinical data workflows, standards, and infrastructure. You will be responsible for building scalable, compliant, and high-performing data pipelines that support mission-critical healthcare applications.
Key Responsibilities:
Design, build, and maintain data pipelines using Apache Airflow and Python to process and manage healthcare data.
Programmatically trigger and manage Airflow DAGs using Python, ensuring dynamic and reliable pipeline orchestration.
Ingest, normalize, and transform data from EHR systems (e.g., Epic Clarity, HL7, FHIR resources).
Work extensively with SMART on FHIR APIs for clinical data integration.
Parse and validate structured and semi-structured healthcare data, including HL7 messages and FHIR bundles.
Lead data migration, data transformation, and performance tuning efforts across various systems and environments.
Implement secure and scalable storage solutions using PostgreSQL, MongoDB, and Azure cloud services.
Ensure compliance with HIPAA and best practices for handling PHI and clinical data privacy.
Collaborate with cross-functional teams (data science, engineering, and clinical operations) to support analytics and care delivery optimization.
Document data models, workflows, and system architecture clearly for maintenance and governance.
Required Skills and Qualifications:
8–12 years of experience in data engineering or backend development, with at least 5+ years focused on healthcare data.
Deep understanding of FHIR (Fast Healthcare Interoperability Resources) is a must-have.
Proven experience in data migration, data transformation, and performance tuning.
Expertise in Python, particularly for building and managing Airflow DAGs and programmatically triggering workflows.
Strong knowledge of EHR systems (e.g., Epic Clarity, Cerner).
Proficient in SMART on FHIR, HL7 standards, and healthcare interoperability protocols.
Advanced working knowledge of PostgreSQL and MongoDB (NoSQL).
Good experience working with Azure Cloud services .
Strong understanding of healthcare data privacy and compliance (HIPAA, PHI).
Experience building efficient, reliable ETL/ELT workflows for large-scale healthcare data.
Preferred Qualifications:
Certifications in FHIR, Azure, or data engineering disciplines.
Experience with Databricks, Apache Spark, or distributed data processing.
Exposure to machine learning or advanced analytics in healthcare settings.
