Senior SDET - Data Remote

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Quanata is on a mission to help ensure a better world through context-based insurance solutions. We are an exceptional, customer centered team with a passion for creating innovative technologies, digital products, and brands. We blend some of the best Silicon Valley talent and cutting‑edge thinking with the long-term backing of leading insurer, State Farm.

Learn more about us and our work at

Quanata, LLC is an insurance technology innovation company that engineers advanced risk prediction and prevention solutions, develops risk‑focused acquisition capabilities, and builds/supports a full‑stack, flexible, digital & increasingly AI‑native insurance platform. This helps our primary clients, State Farm and HiRoad Assurance Company, adapt to evolving market needs. Quanata, LLC is wholly owned and funded by State Farm.

As a company that prioritizes an inclusive and positive culture, we believe the core of our success is in hiring talented people — across disciplines — who want to help us make a quantifiable impact.

Join our Data Platform QA team as a hands‑on engineer focused on ensuring the quality, reliability, and performance of complex data platforms and machine learning workflows.

In this individual contributor role, you’ll design scalable approaches to automated testing and quality assurance across data and ML systems. You’ll partner closely with Data Engineers, ML Engineers, Data Scientists, and Dev Ops teams to identify risks early, strengthen quality throughout the development lifecycle, and ensure data and model outputs can be trusted.

You’ll also help shape testing practices across teams, troubleshoot complex production issues, and mentor other quality engineers while remaining actively involved in hands‑on engineering.

• Design and implement scalable automated testing frameworks for complex data pipelines, processing workflows, and machine learning models.
• Develop comprehensive testing strategies that address data quality, schema integrity, data drift, model accuracy, bias, and regression.
• Validate large‑scale distributed data systems for accuracy, reliability, performance, and resilience.
• Partner across engineering, data science, and operations teams to embed quality throughout the software development and delivery lifecycle.
• Conduct exploratory testing to identify edge cases and risks in new features, data outputs, and machine learning workflows.
• Build continuous validation into automated development and deployment processes.
• Investigate production data and model failures, identify root causes, and help drive effective resolutions.
• Mentor junior SDETs and contribute to broader testing strategies, standards, and quality engineering best practices.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or an equivalent combination of education and relevant experience.
• Typically 6–8 years of software testing experience, including at least 3 years focused on data platform or machine learning testing.

• Strong programming skills with experience building and maintaining automated testing frameworks.
• Hands‑on experience testing complex data platforms, pipelines, and distributed data processing systems.
• Strong understanding of data validation approaches, including data quality, integrity, consistency, and schema validation.
• Understan…