LOCATION:

This will be a hybrid role (1 day a week) working out of our San Carlos, CA location.

POSITION SUMMARY:

Natera is a global leader in cell-free DNA testing, supporting oncology, women’s health, and organ health. Our goal in R&D operations data science is to support R&D activities at Natera through data analytics and the development of intelligent tools to optimize workflows and processes.

As a Senior Data Analyst on the team, you will play a pivotal role in designing, prototyping, and building out a modern, next-generation AI analytics platform to phase out legacy reporting infrastructure. In this role, you will leverage AI developer tools (e.g., Claude Code, Gemini) to significantly accelerate design, scripting, and platform prototyping. However, speed must be paired with engineering rigor: you must possess deep foundational technical expertise to critically review, audit, validate, and refine all AI-assisted output, ensuring production-grade code quality, data accuracy, and system reliability.

PRIMARY RESPONSIBILITIES

Platform Design & Rapid Prototyping: Lead the effort in architecting, iterating, and building out a new R&D analytics platform. Rapidly prototype scalable analytics solutions and modern self-service data tools
AI-Accelerated Development & Rigorous Validation: Proactively utilize advanced AI tools (including Claude Code, Gemini, and coding assistants) to accelerate development cycles, automate repetitive coding tasks, and speed up platform prototyping. Thoroughly review, debug, unit-test, and validate all generated code and analytics logic for correctness, security, and efficiency
Data Pipeline & SQL Engineering: Extract, clean, transform, and model complex multi-source R&D data. Develop and optimize high-performance custom SQL queries and partner with Data Engineers to enhance database scalability and platform performance
Analytics & Insight Generation: Translate operational requirements into actionable insights, automated KPI workflows, and modern analytics interfaces that directly drive R&D decision-making and continuous process improvement
Stakeholder Collaboration: Partner closely with cross-functional R&D stakeholders and operational teams to understand workflows, define analytics requirements, and deliver robust data tools
Data Quality & Governance: Establish validation frameworks, automated monitoring, and data integrity checks to guarantee absolute accuracy and consistency across all analytics platform outputs

QUALIFICATIONS:

Experience: 2+ years of experience in data analysis, analytics engineering, or software/data platform development
AI Tooling Proficiency: High comfort level and practical experience using modern AI developer assistants (e.g., Claude Code, Gemini, GitHub Copilot) to augment coding productivity and rapid iteration
Analytical Rigor & Data Validation: Strong analytical intuition and critical thinking skills, with a proven ability to independently evaluate, verify, and fine-tune data workflows, queries, and automated outputs to maintain high standards of quality and accuracy
Python Proficiency: Advanced proficiency in Python for data analysis, pipeline development, and platform prototyping
Advanced SQL Expertise: Strong expertise in writing, optimizing, and maintaining complex custom SQL queries
Engineering Best Practices: Strong commitment to writing clean, modular, reproducible, and well-documented code using version control (Git)
Agile Problem Solving: Ability to work in a fast-paced, iterative environment, independently troubleshooting complex technical and data challenges
Communication: Strong written and verbal communication skills to collaborate effectively across technical and non-technical teams

Nice to Haves:

Experience with modern cloud data warehouses (e.g., Snowflake) and analytics engineering tools (e.g., dbt)
Experience building custom web-based data applications or internal tools (e.g., Streamlit, Dash, Gradio, or web frameworks)
Domain knowledge in molecular biology, biochemistry, bioinformatics, operations research, or related biotech fields