Senior SDET – Data [Remote-US]

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 DevOps 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.
• Understanding of machine learning concepts, workflows, and model evaluation methods.
• Experience developing automated validation approaches for data and machine learning systems.
• Deep understanding of continuous integration, continuous delivery, and infrastructure automation practices.
• Ability to troubleshoot complex data and model failures and contribute effectively to root cause analysis.

• Ability to collaborate effectively across engineering, data science, and operations teams.
• Strong analytical and problem-solving skills, with the ability to identify risks and edge cases in complex systems.
• Ability to mentor other quality engineers and contribute to shared testing standards and best practices.

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