Natera is seeking an experienced Senior Software Engineer with a deep scientific R&D background in modern data engineering and AI-enabled development. This role focuses on designing and building data products that directly support genomics research and translational science. The ideal candidate will possess strong data engineering skills, a computer science background, and hands-on experience in bioinformatics, genomics, or computational biology, with the ability to work independently in an R&D environment. You will prototype novel data products and ensure they evolve into robust, compliant, and scalable platforms, emphasizing reproducibility, traceability, performance, and scientific usability.

Responsibilities

Design, build, and maintain data products supporting 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 aligned with research patterns and downstream analysis.
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 for analytics, reporting, and downstream applications, maintaining high standards of data quality, accuracy, lineage, and observability.
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 for exploratory, proof-of-concepts, and early-stage initiatives, guiding the transition to production-grade systems.
Implement best practices for data quality, validation, lineage, observability, and reproducibility.
Collaborate with product managers and domain experts to translate requirements into technical solutions.
Establish golden paths (templates, examples, docs) and contribute to shared data product catalogs, patterns, and best practices.
Provide technical guidance and mentorship to mid-level engineers.

Requirements

Bachelor’s or Master’s degree in computer science or bioinformatics, with healthcare or biotech data domain experience preferred.
8+ years of experience in data engineering, designing and maintaining data pipelines and cloud data architectures (e.g., Snowflake, AWS, etc.).
Strong background in bioinformatics, genomics, or computational biology (required), understanding key genomics and bioinformatics data formats (BAM, VCF, FASTQ), common compression techniques, and their storage, delivery, and management needs.
Demonstrated experience supporting scientific R&D, Lab workflows, and research teams with production-grade data systems.
Strong proficiency in Python, SQL, and distributed processing frameworks (Spark or equivalent).
Experience with modern orchestration tools (Airflow, dbt, Dagster).
Experience leveraging AI-assisted development tools (e.g., LLM copilots) to accelerate data solution development.
Familiarity with building data products that support analytics, ML, or AI applications.
Strong data modeling expertise (dimensional, normalized, healthcare-specific schemas).
Experience implementing CI/CD for data pipelines and IaC (Terraform, CloudFormation); Knowledge of data observability, testing, and data quality frameworks.
Demonstrated ownership of production-grade data systems and end-to-end pipeline lifecycle.
Ability to evaluate emerging data and AI technologies and recommend scalable solutions.
Proven ability to operate effectively in fast-paced environments, balancing speed, rigor, and compliance.
Strong written and verbal communication skills with the ability to collaborate across engineering, analytics, and business stakeholders.
Experience working with healthcare, life sciences, or other highly regulated data, including hands-on HIPAA compliance.