Description
We are seeking a Senior Data Engineer to join our Knowledge & Information team. As a Senior Data Engineer, you will play a critical role in shaping the core data products that fuel our Healthcare Map. This role is essential for transforming massive, complex healthcare datasets into performant, trustworthy, and usable data assets that directly power both customer-facing applications and internal product innovation.
Mission: The Senior Data Engineer will design, build, operate, and improve large-scale data pipelines and foundational data products that power Komodo’s Healthcare Map, analytics products, and downstream AI/ML-enabled use cases. This is a hands-on engineering role focused on processing complex healthcare data at scale, improving reliability and performance, and contributing to the technical direction of core data systems.
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.
Preferred skills and experience include:
- 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.