Head of Data Engineering & Platform, Real-World Data (RWD)

Head Of Data Engineering & Platform, Real-World Data (RWD)

US Remote

Position Summary

Natera is seeking a leader to head Data Engineering and Platform for Real-World Data (RWD). This role will in partnership with Product drive the strategy and roadmap for the RWD platform and lead the development, operation, and delivery of data products that support clinical, research, and business priorities. The ideal candidate brings strong engineering leadership, deep technical expertise, and experience building reliable, scalable data platforms in healthcare or life sciences environments.

This leader will oversee a team focused on data engineering, data platform development, and data delivery, and will partner closely with product, bioinformatics, analytics, and AI teams to build data solutions that are useful, reliable, and easy to consume across the organization.

Responsibilities

• Lead, mentor, and grow a team of data engineers and SQA, with a focus on strong engineering practices, career development, and operational ownership.

• Define and drive the strategy and roadmap for the Real-World Data platform, including capabilities that support research and analytics, rapid data mart development, and secure data de-identification and sharing.

• Partner with product, bioinformatics, analytics, AI, software engineering, and infrastructure teams to define requirements, priorities, data contracts, and delivery models, and translate them into scalable platforms, production-grade pipelines, and reusable data products for clinical, molecular, genomic, and real-world data.

• Lead the delivery of curated datasets, data marts, and consumer-facing data solutions, defining data SLAs and service expectations to ensure products are reliable, timely, well-documented, and fit for downstream use.

• Provide technical leadership across data platform architecture, system design, and engineering practices, including data quality, validation, metadata, lineage, observability, testing, and operational support.

• Drive execution across the team through planning, prioritization, estimation, hiring, and delivery management, while balancing long-term platform investments with near-term business needs.

Qualifications

• Bachelor's, Master's or PhD degree in Computer Science, Engineering, Bioinformatics, or a related technical field; advanced degree is a plus.

• 8+ years of experience in software engineering or data engineering, including substantial experience building production-grade data platforms, pipelines, and data products, along with 4+ years of engineering leadership experience.

• Strong hands-on expertise in Python, SQL, and modern data engineering practices, including pipeline development, data modeling, workflow orchestration, testing, code reviews, and agile delivery.

• Deep experience designing and operating scalable, secure, and observable data platforms in cloud environments, with AWS strongly preferred.

• Experience with modern data warehouse and query technologies such as Snowflake, Redshift, PostgreSQL, Athena, or similar platforms, along with strong knowledge of relational and analytical data systems.

• Experience working with healthcare, clinical, genomic, or real-world data domains, including environments requiring strong governance, de-identification, and secure handling of sensitive data.

• Experience building and operating data platforms with strong DevOps and operational practices, including CI/CD, observability, infrastructure automation, production support, and secure access patterns.

• Proven ability to lead and develop strong teams while remaining technically credible, with strong communication, collaboration, and execution skills.

• Experience partnering with research, bioinformatics, scientific, or analytical users to understand workflow needs and deliver effective data products and platform capabilities is strongly preferred.

• Hands-on experience with bioinformatics pipelines, molecular and clinical data, and healthcare data models such as FHIR or OMOP is strongly preferred.