Engineering Manager, Data

Responsibilities

• Build and Lead the Team: Hire and manage a team of data engineers (ranging from staff to mid-level). Establish team processes, engineering culture, and development practices from day one. Coach and grow engineers across multiple levels.
• Own the AI Data Platform: Take full technical and operational ownership of the platform that ingests, de-identifies, transforms, and serves clinical imaging data (e.g., DICOM) and reports. Ensure the platform is reliable, scalable, and secure.
• Define and Execute the Technical Roadmap: Set the technical direction for the platform. Prioritize work across scalability improvements, new capabilities, and technical debt reduction in alignment with business OKRs.
• Ensure Data Quality and Compliance: Implement data quality frameworks, validation checks, and governance processes. As the steward of highly sensitive medical data, ensure all aspects of the platform are compliant with HIPAA and other applicable regulations.
• Drive Cross-Functional Collaboration: Serve as the primary point of contact for the AI, Product, Clinical, and Regulatory teams on all data platform matters. Translate their needs into a prioritized roadmap and ensure the team delivers reliably against commitments.

Required Qualifications

• Mission-driven and passionate about building foundational technology to improve healthcare
• 8+ years of experience in software or data engineering, with at least 2 years in an engineering management role and proven track record of building engineering teams or scaling teams through a significant growth phase
• Strong technical foundation in data engineering - comfortable reviewing system designs, making architectural decisions, and contributing code in Python and SQL when needed
• Experience with cloud-native data platforms and modern data infrastructure (e.g., data lakes, data warehouses, ETL/ELT pipelines, workflow orchestration tools like Airflow/Prefect/Dagster)
• Demonstrated ability to manage cross-functional stakeholders and translate business needs into engineering priorities
• Experience operating in a fast-paced, high-growth environment where priorities shift and ambiguity is the norm
• BS in Computer Science or a related field, or equivalent real-world experience

Preferred

• Experience in healthcare, healthtech, or another regulated industry (HIPAA, FDA, GDPR)
• Experience with MLOps or building infrastructure that supports machine learning workflows
• Experience with multi-cloud environments and cloud cost optimization

Compensation

Base salary: $190,000-$230,000, plus bonus, equity and benefits.