Senior Data Engineer

Komodo Health is dedicated to reducing the global burden of disease by leveraging data insights. The Senior Data Engineer will play a crucial role in transforming complex healthcare datasets into reliable data products that enhance the Healthcare Map and support internal product innovation.

Responsibilities:

• Build, operate, and optimize large-scale production data pipelines using Python, SQL, Airflow, cloud infrastructure, and distributed processing frameworks — including robust data quality checks, validation, lineage, observability, monitoring, and alerting
• Design and scale agentic data acquisition and extraction systems for complex, unstructured public data sources; develop LLM-powered Human-in-the-Loop (HITL) pipelines for data extraction and curation
• Transform healthcare claims, EHR, non-claims-based, and reference datasets into trusted, performant Healthcare Map data products and serving-ready data assets
• Contribute to system design, architecture, code quality, testing, documentation, CI/CD, and rotational production support — including debugging complex data, system, and performance issues across computationally intensive workflows
• Partner with Data Product Quality, Product, Platform, and Engineering teams to translate healthcare data needs into scalable technical solutions that enable downstream analytics, product, and AI/ML use cases

Requirements:

• Strong hands-on experience building, operating, and debugging production-grade data pipelines at scale in AWS, with sharp instincts for data quality, reliability, root-cause analysis, and production troubleshooting
• Advanced Python and SQL skills; experience with Airflow or similar orchestration tools and Spark or comparable distributed processing frameworks
• Ability to communicate technical trade-offs clearly and collaborate across engineering, product, and data teams
• Comfort using AI-assisted engineering tools for productivity, debugging, documentation, and technical exploration
• Healthcare data experience is a plus, but not required
• Ability to optimize high-scale data architectures for performance, cost, versioning, and large-volume productization; experience applying AI or agentic workflows to engineering, data quality, delivery, or operations
• Proven success in high-growth or ambiguous environments that require balancing architecture, speed, and quality