Senior SDET – Data [Remote-US]

Role overview Join a remote-first data platform quality assurance team as a hands-on engineer focused on the reliability and performance of complex data platforms and machine learning workflows. This individual contributor role centers on designing scalable automated testing and quality assurance approaches across data and ML systems, partnering with data engineers, ML engineers, data scientists, and DevOps to identify risks early and embed quality throughout the development lifecycle. The work blends hands-on engineering with mentorship, shaping testing practices across teams and driving root-cause analysis on complex production issues.

Responsibilities - Design and implement scalable automated testing frameworks for complex data pipelines, processing workflows, and machine learning models. - Develop comprehensive testing strategies covering 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 surface edge cases and risks in new features, data outputs, and ML workflows. - Build continuous validation into automated development and deployment processes; investigate production data and model failures and drive effective root-cause resolutions. - Mentor junior SDETs and contribute to broader testing strategies, standards, and quality engineering best practices.

Requirements - Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or 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, plus strong understanding of data validation approaches including data quality, integrity, consistency, and schema validation. - Understanding of machine learning concepts, workflows, and model evaluation methods, plus experience developing automated validation approaches for data and ML systems. - Deep understanding of continuous integration, continuous delivery, and infrastructure automation, with the ability to troubleshoot complex data and model failures.

Nice to have - Experience evaluating machine learning models for interpretability, fairness, and bias. - Familiarity with machine learning operations, model lifecycle practices, and monitoring the health and performance of production data pipelines and ML systems. - Knowledge of data governance and compliance practices.

Benefits and work setup - Target salary range: $198,000 to $281,000, depending on skills and experience. - Medical, dental, vision, life insurance, and supplemental income plans; Headspace subscription; monthly wellness allowance; 401(k) with company match. - $2,000 one-time home-office setup payment plus a fully provisioned MacBook Pro. - Four weeks of PTO accrued in the first year; 12 weeks of fully paid parental leave for birthing and non-birthing parents. - Up to $5,000 annually for professional learning, plus LinkedIn Learning and BetterUp coaching. - Remote-first within the U.S.; occasional travel may be requested. Core collaboration hours run 9 AM to 2 PM Pacific.