Senior Data Platform Engineer

The Opportunity at Komodo Health

Join Komodo Health's Data Foundations team and 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. By building and scaling our foundational data systems, you will directly enable the transparency and efficiency required to drive better health outcomes across the industry.

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.

Key Responsibilities

• Build, operate, and optimize large-scale production data pipelines using Python, SQL, Airflow, cloud infrastructure, and distributed processing frameworks.
• Transform massive healthcare claims, EHR, and reference datasets into trusted, performant Healthcare Map data products and serving-ready data assets.
• Strengthen pipeline reliability through data quality checks, validation, lineage, observability, monitoring, and alerting.
• Debug 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.
• Contribute to system design, architecture, code quality, testing, documentation, CI/CD, and rotational production support.
• Enable downstream analytics, product, and AI/ML use cases through high-quality, well-modeled, reliable data.

What you bring to Komodo Health:

• Healthcare data experience across claims, clinical, RWE, provider, patient, or life sciences datasets, including coding systems such as ICD-10, CPT, NDC, or NPI.
• Strong hands-on experience building, operating, and debugging production-grade data pipelines at scale.
• Advanced Python and SQL skills, with experience in Airflow or similar workflow orchestration tools.
• Experience with Spark or comparable distributed data processing frameworks.
• Proven experience designing and operating data solutions in AWS.
• Strong instincts for data quality, reliability, root-cause analysis, and production troubleshooting.
• Ability to communicate technical trade-offs clearly and collaborate with engineering, product, and data partners.
• Comfort using AI-assisted engineering tools for productivity, debugging, documentation, and technical exploration.

AI-Augmented Engineering Expectations:

You will be expected to leverage AI-augmented engineering tools, such as ChatGPT, Gemini, or Claude, to improve productivity and technical decision-making. This may include using AI to generate and refine code, accelerate documentation, automate test case creation, debug complex issues, explore unfamiliar technical concepts, and assess architectural trade-offs and risks.