Job Summary
Natera is seeking an experienced Senior Software Engineer with modern data engineering and AI-enabled development skills and a 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.
Key Responsibilities
- 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 data quality, validation, lineage, observability, and reproducibility to enable a trusted 360° view.
- 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 used by other engineers.
- Provide technical guidance and mentorship to mid-level engineers.
Required Qualifications
- 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). Understands key genomics and bioinformatics data formats, such as BAM, VCF, FASTQ, common compression techniques for these file formats, 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.
- Exposure to vector databases, embeddings, semantic search, or RAG-based architectures is a plus.
- Proven ability to operate effectively in fast-paced environments, balancing speed, rigor, and compliance.
- Strong written and verbal communication skills with 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.