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Niche medical-imaging focus reduces applicant pool, but metro Data Engineer title and office location increase competition.
High due to specialized medical-imaging standards, clinical metadata, and PACS/FHIR domain expertise requirements.
Mandatory 7–10 years, healthcare imaging expertise, and specific DICOM/AWS skills make filters stringent.
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Design, develop, and optimize ETL pipelines and data repositories for large-scale medical imaging datasets to ensure efficient ingestion, transformation, storage, and access.
Implement data processing workflows for clinical imaging data (CT, MRI) to extract, standardize, and structure metadata, including integration with PACS, XNAT, and other imaging systems.
Develop scalable, high-performance solutions on AWS for medical imaging data engineering, applying computer vision and machine learning for image analysis and linking imaging with clinical metadata.
7 to 10 years of experience in data engineering focused on healthcare and medical imaging data.
Expertise with medical imaging standards, preferably DICOM, and clinical metadata processing (DICOM headers, HL7, FHIR).
Experience with AWS services such as S3, Lambda, EC2, DynamoDB, and Redshift for data storage and retrieval.
Work Experience Required: 7 to 10 years
Experience in building and optimizing ETL pipelines and data repositories specifically for medical imaging datasets within healthcare.
Strong knowledge of imaging systems integration (e.g., PACS, XNAT) and clinical data standards to enable robust metadata extraction and standardization.
Proven operational experience deploying scalable medical imaging data engineering solutions using AWS cloud infrastructure for clinical environments.