Senior Data Engineer - Healthcare Data & Audience Applications
$140,000–$160,000 year
On-siteSan Francisco, California, United States
Job Summary
Design and operate production-grade pipelines for healthcare, identity, and campaign-performance data using Python, SQL, Airflow, Snowflake, and EMR. Build maintainable data models, governed views, and reusable datasets to support provider identity, claims, and audience discovery. Implement Airflow workflows with dependencies, retries, and data-quality checks while applying privacy-by-design practices for PHI/PII. Partner with product and analytics teams to translate business requirements into resilient technical solutions and troubleshoot production issues. Collaborate with the Lead Data Engineer on code reviews and mentor less-experienced engineers.
Required Qualifications
- 5–8 years of hands-on data engineering experience
- experience working with healthcare data such as provider/HCP, claims, prescription, patient/DTC, or healthcare audience datasets
- Strong Python skills
- expert SQL skills
- demonstrated experience building transformations
- demonstrated experience optimizing queries
- demonstrated experience diagnosing data issues
- Hands-on experience with AWS data services, especially S3
- experience with a modern cloud data warehouse
- experience with Snowflake
- experience with Airflow
- experience with EMR
- Experience with data modeling
- experience with schema evolution
- experience with batch processing
- experience with orchestration
- experience with testing
- experience with CI/CD
- experience with production support practices
- Proven ability to work with large, complex datasets
- Deep, practical knowledge of HIPAA
- knowledge of PHI/PII handling
- knowledge of privacy-by-design controls
- knowledge of the operational requirements of regulated healthcare data environments
Desired Qualifications
- experience working with healthcare data providers
- experience with identity ecosystems
- experience with tokenization
- experience with clean rooms
- experience with privacy-enhancing technologies
- Experience with data cataloging
- experience with lineage
- experience with observability
- experience with data-quality frameworks
- Experience with Docker
- experience with Kubernetes/EKS
- experience with infrastructure as code
- experience with cloud deployment workflows
- Experience supporting reporting, attribution, or measurement products tied to campaign or business outcomes
- Exposure to ML/AI-enabled data products or analytics workflows
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