Senior Data Engineer, Platform & Pipelines

Natera is seeking a Senior Data Engineer to join their Therapeutics & Innovations group. This role focuses on developing a shared, platform-level data system that includes multi-modal data ingestion, backend services, and AI-enabled data access.

Responsibilities:

• Architect, implement, and maintain data ingestion and transformation pipelines using modern workflow orchestration tools (e.g. Dagster)
• Identify, catalog, and integrate internal and external data sources used across research efforts
• Operationalize bioinformatics pipelines that support large-scale batch processing, incremental updates, and backfills within AWS
• Normalize and structure heterogeneous data into consistent, reusable representations that support downstream analysis, modeling, and querying
• Populate and maintain patient-centric data models in shared storage systems (e.g., graph and relational databases)
• Collaborate with backend and AI engineers to design data-access patterns that support analytics applications and AI-driven interactions
• Contribute to backend services and APIs that expose integrated data to internal tools and applications
• Participate in the evolution of AI-enabled analysis workflows, including tooling that supports LLM- or agent-based interactions with data
• Contribute to system-level design decisions around data flow, service boundaries, reliability, and scalability
• Write clean, tested, and well-documented Python code that meets production software engineering standards
• Debug and resolve complex data quality, pipeline, backend, and infrastructure issues in a distributed environment

Requirements:

• BS in Computer Science, Bioinformatics, Computational Biology, or a related field, MS preferred
• 4+ years of experience in production data engineering or software engineering
• Independently drive technical solutions from high-level goals, exercising judgment in system design, implementation, and tradeoff evaluation
• Strong proficiency in Python, with experience writing maintainable, production-quality code across data and backend contexts
• Extensive experience with software engineering fundamentals, design patterns, version control, CI/CD, Docker, and automated testing
• Experience designing and operating workflow orchestration systems (Dagster preferred; Airflow, Prefect, or similar acceptable)
• Experience building or contributing to backend services (e.g., FastAPI or similar frameworks)
• Hands-on experience with AWS services commonly used in data and backend systems (e.g., S3, ECS, Batch, Lambda)
• Experience deploying and operating large-scale data or bioinformatics pipelines in AWS, including managing throughput, cost, and operational reliability
• Experience with relational databases (Postgres, MySQL) and/or graph databases (Neo4j), including schema and query design
• Experience contributing to system-level architecture, including data modeling, service boundaries, and operational robustness
• Ability to work effectively with scientists, bioinformaticians, and ML practitioners in an R&D environment
• Experience integrating machine-learning inference outputs into data pipelines
• Familiarity with LLM-based agents and associated frameworks such as LangChain
• Familiarity with bioinformatics data formats and pipelines (e.g., FASTQ, BAM/CRAM, VCF, RNAseq, WES/WGS)
• Experience with infrastructure as code (Terraform)
• Experience with DNAnexus
• Understanding of genomics, proteomics, or other omics data types and their downstream analytical use cases
• Ability to evaluate build-vs-buy tradeoffs in fast paced environments