Data Engineer

Function Health empowers people to live longer, healthier lives through proactive, data-driven healthcare by aggregating and analyzing comprehensive health data. The Data Engineer will design, build, and scale secure, compliant, cloud-native data infrastructure and pipelines supporting imaging, biomarkers, analytics, and AI ecosystems.

Responsibilities:

• Design and operate scalable data pipelines and orchestration systems supporting imaging and biomarker data (e.g., Airflow, cloud services, distributed compute platforms)
• Contribute to PHI-safe, compliant data infrastructure aligned with healthcare standards (HIPAA, GDPR, etc.)
• Enhance performance and fault-tolerance across distributed data workflows
• Partner with the data management lead to integrate DICOM and data pipelines into the broader data lake and analytics stack
• Implement robust data validation, observability, and monitoring systems for production pipelines
• Collaborate with data science, ML, and product teams to ensure data reliability and accessibility
• Support automated ingestion, transformation, cataloging, and access patterns for downstream analytics and ML use cases

Requirements:

• 1-2+ years of experience in data engineering, ETL development, or cloud-based data orchestration
• 1-2+ years of experience in data science, analytics, or applied research, IF hold a PHD/published research
• Strong proficiency in Python and SQL
• Hands-on experience with workflow orchestration tools (Airflow, Prefect, or similar)
• Experience with cloud platforms and services (AWS, Azure, GCP familiarity is a plus)
• Experience building on Databricks and working with distributed processing frameworks (Apache Spark, Dask, or similar)
• Solid understanding of data validation, observability, testing, and production best practices
• Experience with healthcare, imaging, or PHI-sensitive data is a strong plus
• Strong communication and documentation skills; comfortable working across technical teams