OverviewWe are partnering with an innovative healthcare technology company that is transforming how disease risk is understood and managed. With strong clinical validation, regulatory momentum, and real-world adoption, the organization is scaling its data capabilities to support continued growth across AI-driven products and clinical research.
This is a
high-impact leadership role where you will own clinical data end-to-end and build out the data engineering function. It's ideal for a senior individual contributor ready to step into leadership, someone who wants to
mentor a team while remaining hands-on in building scalable data systems.
What You'll Do - Own clinical data pipelines end-to-end, including ingestion, transformation, storage, and governance
- Design and build scalable pipelines for clinical and medical imaging (DICOM) data used in ML model development
- Lead, mentor, and grow the data engineering team, establishing best practices and standards
- Serve as the internal subject matter expert on clinical data assets
- Implement data quality frameworks, validation checks, and monitoring systems
- Establish data lineage and governance processes to support regulatory and audit requirements
- Develop and maintain a comprehensive data catalog (datasets, schemas, sources, quality metrics)
- Partner closely with ML and software engineering teams to support model development and production systems
What You Bring Required - Master's degree with 7+ years of experience, or PhD with 4+ years, in a quantitative field (CS, Data Science, Biomedical Informatics, etc.)
- Strong proficiency in Python and SQL
- Proven experience building and owning modern data pipelines (e.g., Prefect, Metaflow, or similar)
- Hands-on experience with AWS data infrastructure (S3, Athena, RDS, Glue, etc.)
- Familiarity with DICOM and medical imaging workflows
- Experience supporting ML teams with data infrastructure for model development and deployment
- Strong analytical skills and ability to work with complex clinical datasets
- Excellent communication skills, able to translate data insights across technical and non-technical audiences
Preferred - Experience with mammography or medical imaging domains
- Familiarity with FDA regulatory processes (SaMD or similar)
- Knowledge of data governance, lineage, and compliance frameworks
- Experience with HIPAA, PHI handling, and data de-identification
- Background in clinical research, epidemiology, or biostatistics
- Experience with data visualization tools for internal reporting
- Exposure to infrastructure-as-code tools (Terraform, CloudFormation)
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