Staff Data Engineer

Komodo Health

5h • Hybrid

About The Position

We Breathe Life Into Data
At Komodo Health, our mission is to reduce the global burden of disease. And we believe that smarter use of data is essential to this mission. That’s why we built the Healthcare Map — the industry’s largest, most complete, precise view of the U.S. healthcare system — by combining de-identified, real-world patient data with innovative algorithms and decades of clinical experience. The Healthcare Map serves as our foundation for a powerful suite of software applications, helping us answer healthcare’s most complex questions for our partners. Across the healthcare ecosystem, we’re helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease.
As we pursue these goals, it remains essential to us that we stay grounded in our values: be awesome, seek growth, deliver “wow,” and enjoy the ride. At Komodo, you will be joining a team of ambitious, supportive Dragons with diverse backgrounds but a shared passion to deliver on our mission to reduce the burden of disease — and enjoy the journey along the way.
The Opportunity at Komodo Health:
Team: Data Foundations owns the core data products that power Komodo’s Healthcare Map. We design and deliver the foundational data assets, models, and serving capabilities that make healthcare data usable, performant, and trustworthy for customers and internal product teams. Our work reaches customers directly and also powers consuming applications through APIs, analytics layers, and other data delivery surfaces.
Mission:This role exists to architect and deliver the foundational data products and serving capabilities that turn complex healthcare data into trusted, production-ready assets for customers and applications. As a Staff-level IC, you will set technical direction across data modeling, productization, analytics enablement, and scalable delivery patterns, partnering with Product, Data Science, and consuming application teams to make Healthcare Map data usable at enterprise scale.
Komodo Health leverages the latest engineering technology to tackle healthcare’s biggest challenges by transforming extraordinary amounts of data into meaningful insights. As a Staff Data Engineer, you will drive the development of state-of-the-art foundational products that unlock the potential of our Healthcare Map, fostering a culture of technical excellence and transparency across the organization.
Looking back on your first 12 months at Komodo Health, you will have accomplished…
Architectural Advancement: Managed and delivered high-impact projects that introduced new technical and data services to market, significantly improving pipeline performance.
Innovation in Healthcare Data: Developed a deep understanding of domain-specific challenges and introduced innovative algorithms that address complex patient journey mapping.
Collaborative Roadmap Delivery: Partnered across Data Product and Engineering to define technical requirements and deliver scalable, production-grade solutions that meet business needs.
Platform Hardening: Built and hardened core Data Foundations components, measurable through improved reliability, observability, and cost-efficiency of our Healthcare Map products.
Technical Multiplication: Mentored and up-leveled the engineering team, raising the bar for code quality and system design through coaching and rigorous design reviews.

Requirements

• Data Product Engineering Expertise: Extensive experience building production-grade, large-scale data products, services, and analytical systems that serve real customer and business use cases.
• Modern Data Systems Depth: Strong technical depth across SQL, distributed data processing, cloud data platforms, MPP databases, and high-scale compute frameworks such as Spark, Python, Rust, C++, or equivalent technologies.
• Architecture & Systems Design: Demonstrated ability to design data models, serving patterns, platform components, and system architectures for complex, high-volume data environments.
• Healthcare Data Product Judgment: Ability to reason through data quality, identity, longitudinal patient journeys, claims or clinical data complexity, and downstream consumption needs.
• AI & ML Enablement: Experience designing data workflows, feature pipelines, evaluation datasets, or infrastructure that supports AI/ML training, inference, experimentation, and monitoring.
• Analytical & Statistical Rigor: Strong ability to use data analysis, statistical reasoning, hypothesis testing, and experimental design to validate product quality and business impact.
• Technical Communication: Ability to explain technical decisions, trade-offs, risks, and delivery status clearly to engineers, product partners, data scientists, and senior stakeholders.
• AI-Augmented Engineering: Ability to use AI tools such as ChatGPT, Gemini, Cursor, Claude, or similar systems to improve engineering productivity, design quality, testing, documentation, and decision-making.

Nice To Haves

• Healthcare Data Experience: Experience with claims, clinical, RWE, provider, patient, or life sciences data, including familiarity with coding systems such as ICD-10, CPT, NDC, RxNorm, NPI, or taxonomy data.
• Data Product Delivery: Experience building and operating data products that are consumed by customers, analytics users, APIs, applications, or serving layers.
• High-Scale Data Architecture: Experience designing systems for large-volume data processing, productization, versioning, delivery, performance optimization, and cost efficiency.
• Applied AI / Agentic Workflows: Experience using, designing, or integrating AI-enabled workflows to improve engineering productivity, data quality, extraction, curation, testing, or product delivery.
• Fast-Growth Execution: Experience operating in high-growth or ambiguous environments where technical leaders must balance architecture, delivery, quality, and speed.

Responsibilities

• Data Product Development & Delivery: Architect, build, and deliver scalable Healthcare Map data products that power direct customer use cases, APIs, analytics surfaces, serving layers, and internal applications.
• Scalable Data Systems: Design and implement high-performance data processing and serving patterns across large-scale healthcare datasets, using the right tools for the problem across SQL, Python, Spark, Rust, C++, and emerging AI-enabled engineering workflows.
• Foundational Data Models: Create shared data models, productized datasets, reusable libraries, and technical standards that become the foundation for downstream product, analytics, and application teams.
• Serving & Consumption Enablement: Build data products that are easy to consume through APIs, serving layers, exports, analytics environments, and customer-facing delivery mechanisms.
• Cross-Functional Product Delivery: Partner with Product, Data Science, Quality, Platform, and application teams to translate complex healthcare use cases into production-grade technical designs and execution plans.
• Technical Leadership: Lead complex, multi-quarter initiatives, making clear trade-offs across performance, scalability, maintainability, cost, reliability, and time-to-market.
• Quality, Observability & Trust: Define and implement data quality checks, validation frameworks, observability, lineage, monitoring, and alerting to ensure Healthcare Map products are accurate, explainable, and reliable.
• Engineering Excellence: Raise the bar for system design, code quality, documentation, testing, CI/CD, and operational readiness across the team.
• Engineering Leverage: Mentor engineers through design reviews, technical deep dives, pairing, and architectural guidance, helping the team make better decisions at scale.

Benefits

• comprehensive health, dental, and vision insurance
• flexible time off and holidays
• 401(k) with company match
• disability insurance and life insurance
• leaves of absence in accordance with applicable state and local laws and regulations and company policy

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed