Senior Data Engineer

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.
• Deliver architectural improvements that increase pipeline performance, scalability, and system efficiency.
• Improve the reliability, observability, and cost-efficiency of core Data Foundations systems.
• Mentor teammates and contribute through design reviews and engineering best practices.

Requirements

• Healthcare data experience across claims, clinical, real-world evidence, 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 using 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.

Nice-to-haves

• Experience delivering external-facing data products through customers, APIs, serving layers, or production access patterns.
• 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.
• Success in high-growth or ambiguous environments requiring balance among architecture, speed, and quality.

Compensation and benefits

• Annual base pay: $170,000–$230,000, depending on geographic location.
• San Francisco Bay Area and New York City: $196,000–$230,000.
• All other US locations: $170,000–$200,000.
• May be eligible for performance-based bonuses and equity awards.
• Benefits include comprehensive health, dental, and vision insurance; flexible time off and holidays; 401(k) with company match; disability insurance; life insurance; and applicable leaves of absence.