Head of Data Engineering & Platform, Real-World Data (RWD)
On-site · North Carolina, United States
Job Summary
Lead the Data Engineering and Platform for Real-World Data (RWD) at Natera, overseeing data engineers and SQA, defining the strategy and roadmap for the RWD platform, and delivering data products that support clinical, research, and business priorities. Partner with product, bioinformatics, analytics, AI, software engineering, and infrastructure teams to translate requirements into scalable, production-grade pipelines and reusable data products for clinical, molecular, genomic, and real-world data. Provide technical leadership across platform architecture, data quality, governance, metadata, lineage, observability, testing, and operational support. Drive planning, prioritization, hiring, and delivery management to balance long-term platform investments with near-term needs.
Required 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
- 4+ years of engineering leadership experience
- Strong hands-on expertise in Python and SQL
- Experience with modern data warehouse and query technologies (Snowflake, Redshift, PostgreSQL, Athena, etc.)
- Experience operating scalable, secure, and observable data platforms in cloud environments (AWS strongly preferred)
- Experience in healthcare, clinical, genomic, or real-world data domains with governance and de-identification requirements
- DevOps and operational practices (CI/CD, observability, infrastructure automation, production support)
- Strong communication, collaboration, and execution skills
- Experience partnering with research, bioinformatics, or analytical users to deliver data products
- Hands-on experience with bioinformatics pipelines, molecular and clinical data, and healthcare data models (FHIR or OMOP) is strongly preferred
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