Job Summary:
Natera is seeking an experienced Senior Software Engineer with modern data engineering and AI-enabled development skills with deep scientific R&D background to design and build data products that directly support genomics research and translational science. This role is intended for someone who already understands how research organizations operate, how genomic data flows from experiment to insight, and how to engineer data systems that accelerate discovery without compromising rigor or compliance.
The ideal candidate combines strong data engineering skills with a computer science background and hands-on experience in bioinformatics, genomics, or computational biology, and the ability to work independently in an R&D environment. You will also be comfortable moving quickly to prototype novel data products while ensuring solutions evolve into robust, compliant, and scalable platforms. You will bring an internalized sense of what “good” looks like for research data: reproducibility, traceability, performance, and scientific usability.
Design, build, and maintain the data products that support R&D, analytics, Lab and scientific workflows, from initial design through deployment and iterations
Build and maintain data pipelines for large and complex datasets, from raw inputs through derived and analysis-ready datasets.
Apply domain knowledge in genetics and bioinformatics to design data models, schemas, and abstractions that align with real research patterns and downstream analysis needs.
Design and enforce de-identification and privacy-preserving architectures that meet HIPAA and related regulatory requirements while remaining usable for research.
Design scalable data models to power analytics, reporting, and downstream applications. Maintain high standards of data quality, accuracy, lineage, and observability across data pipelines.
Partner closely with R&D scientists, bioinformatics teams, and software engineers to translate research needs into well-structured, reusable data assets.
Optimize storage, retrieval, and lifecycle management for large scientific files (E.g. sequencing data, intermediate artifacts, derived datasets).
Drive rapid prototyping efforts to support exploratory, proof-of-concepts, and early-stage initiatives, while guiding the transition to production-grade systems.
Implement best practices for