Healthcare Data Engineer (AA - 11112025 - PTHDE)
Summary
Builds and maintains scalable ETL/ELT pipelines for healthcare data, integrating sources like EHRs and pharmacy datasets while ensuring compliance with HIPAA/GDPR and mapping data to medical standards (ICD-10, SNOMED CT).
Position: Healthcare Data Engineer
Number of hours: 20 hours/week
Schedule: UK Time Zone - 9AM - 5PM
Key Responsibilities:
ETL Pipeline Development: Design, implement, and manage scalable and reliable ETL/ELT data pipelines to process diverse healthcare data from various sources.
Data Integration: Extract and consolidate data from disparate sources, including electronic health records (EHRs), real-world datasets, pharmacy sell-out data, and disease-specific surveys.
Data Transformation & Cleansing: Cleanse, validate, and standardize raw healthcare data. Map data to standard medical terminologies (e.g., ICD-10, SNOMED CT, LOINC), remove duplicates, and resolve inconsistencies to ensure high data quality.
Cloud Management: Utilize AWS or Google Cloud services to build, deploy, and monitor data processing solutions and storage infrastructure.
Compliance and Security: Implement and maintain strict data security, privacy, and governance protocols to ensure compliance with regulations such as HIPAA and GDPR, including encryption, access control, and audit trails.
Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand data requirements and ensure the data architecture supports advanced analytics and business intelligence needs.
Qualifications:
Proven experience as a Data Engineer, preferably within healthcare or life sciences.
Strong expertise in designing and managing ETL/ELT processes and data pipelines.
- Hands-on experience with at least one major cloud platform:
AWS: Proficiency with services such as AWS Glue, Amazon S3, AWS Lambda, Amazon Redshift, and AWS HealthLake.
Google Cloud: Proficiency with services like Cloud Healthcare API, BigQuery, Dataflow, and Dataproc.
Solid understanding of healthcare data standards (e.g. HL7, FHIR, DICOM) and data interoperability challenges.
Proficiency in programming languages such as Python and PySpark, and experience with SQL.
Knowledge of data security best practices and experience implementing measures to protect sensitive health information.
