Clinical Data Engineering Lead

Overview

We 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)