Staff Data Engineer

Chamber is a health technology company partnering with independent cardiologists to improve cardiovascular care through technology, data, and operational tools. The Staff Data Engineer will design and implement scalable, secure data architecture, including ETL pipelines, integrations, cloud infrastructure, and data reliability solutions that support healthcare products and analytics.

Responsibilities:

• Design, build, and maintain scalable ETL pipelines and data models to support our analytics and product needs
• Develop robust data integration workflows, enabling seamless data exchange between internal systems and external data sources
• Optimize and scale cloud-hosted databases and data infrastructure to handle complex healthcare datasets efficiently
• Ensure data security and compliance with healthcare industry standards (e.g., HIPAA, HITRUST)
• Collaborate with product, engineering, and clinical teams to define and implement data requirements
• Create documentation and implement monitoring solutions to ensure data reliability and transparency

Requirements:

• 7+ years of experience as a data engineer or similar role, with at least several years of experience in health tech
• Strong expertise in SQL and Python
• Hands-on experience with modern data warehousing technologies (e.g., Snowflake, BigQuery, Redshift)
• Experience with data pipeline and workflow orchestration tools (e.g., Temporal, Airflow, Prefect, Dagster)
• Experience with cloud platforms such as AWS (preferred), GCP, or Azure
• Solid understanding of data security principles and familiarity with healthcare data compliance requirements
• Experience with AI software development tools (e.g., Claude Code, Cursor) and an AI forward mindset
• Demonstrated ability to work with messy, complex data and transform it into actionable insights
• Strong communication skills and the ability to work effectively in cross-functional teams
• Must be based in the United States and authorized to work without sponsorship
• Experience with DBT for data transformation and modeling
• Experience with lightweight, cloud-hosted secure data transfer solutions such as SFTPGo
• Experience with healthcare interoperability standards such as FHIR and HL7 event formats
• Experience with claims data sharing formats (e.g., 837, 834, CCLF) and modeling clinical quality measures (e.g., CQL)
• Experience working with the Tuva Project
• Understanding of common clinical quality measure definitions and rules engine logic