Software Engineer II (Data & AI Solutions) - Work From Home

About the Company

Natera is a global leader in cell-free DNA (cfDNA) testing, focused on oncology, women’s health, and organ health. The company aims to make personalized genetic testing and diagnostics part of standard care, supporting earlier and more targeted interventions to improve health outcomes.

Natera brings together professionals across statistics, genetics, medicine, laboratory science, software engineering, and other disciplines to advance genetic testing and disease management.

About the Role

Natera is seeking a Senior Software Engineer with strong data engineering expertise and a background in genomics, bioinformatics, or computational biology. This role focuses on designing and building data products that support scientific research, laboratory workflows, analytics, and translational science.

You will work closely with R&D scientists, bioinformatics specialists, and engineering teams to turn research needs into reliable, scalable data systems. The position combines rapid prototyping with the development of production-ready platforms, with an emphasis on data quality, reproducibility, traceability, privacy, and regulatory compliance. You will also help establish shared engineering practices and provide technical guidance to other engineers.

Key Responsibilities

• Design, build, deploy, and maintain data products that support R&D, analytics, laboratory operations, and scientific workflows.
• Develop and maintain data pipelines that transform raw inputs into derived and analysis-ready datasets.
• Apply knowledge of genetics and bioinformatics to create data models, schemas, and abstractions that reflect research workflows and downstream analysis requirements.
• Design and implement de-identification and privacy-preserving architectures that meet HIPAA and related regulatory requirements while supporting research needs.
• Build scalable data models for analytics, reporting, and downstream applications.
• Maintain data quality, accuracy, lineage, observability, and reproducibility across pipelines.
• Collaborate with R&D scientists, bioinformatics teams, and software engineers to develop reusable data assets.
• Optimize storage, retrieval, and lifecycle management for large scientific files, including sequencing data, intermediate artifacts, and derived datasets.
• Prototype solutions for exploratory research, proof-of-concept projects, and early-stage initiatives, while guiding their transition to production-ready systems.
• Establish data validation, testing, lineage, and observability practices that support a trusted, comprehensive view of research data.
• Work with product managers and domain experts to translate requirements into practical technical solutions.
• Develop reusable templates, examples, documentation, and shared data product catalogs to help other engineers follow established patterns and best practices.
• Provide technical guidance and mentorship to mid-level engineers.
• Evaluate emerging data engineering and AI technologies and recommend scalable solutions.

Required Qualifications

• Bachelor’s or master’s degree in computer science or bioinformatics, with healthcare or biotechnology data experience preferred.
• At least 8 years of experience in data engineering, including designing and maintaining data pipelines and cloud data architectures such as Snowflake and AWS.
• Strong background in bioinformatics, genomics, or computational biology.
• Understanding of genomics and bioinformatics file formats, including BAM, VCF, and FASTQ, along with their compression, storage, delivery, and management requirements.
• Experience supporting scientific R&D, laboratory workflows, and research teams with production-grade data systems.
• Strong proficiency in Python, SQL, and distributed processing frameworks such as Spark or equivalent technologies.
• Experience with modern orchestration tools such as Airflow, dbt, or Dagster.
• Experience using AI-assisted development tools, including LLM copilots, to accelerate data solution development.
• Familiarity with data products that support analytics, machine learning, or AI applications.
• Strong data modeling skills, including dimensional, normalized, and healthcare-specific schemas.
• Experience implementing CI/CD for data pipelines and infrastructure as code using tools such as Terraform or CloudFormation.
• Knowledge of data observability, testing, and data quality frameworks.
• Demonstrated ownership of production data sy…