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Oura is seeking a Senior Data Analytics Engineer for Hardware Quality to join their Hardware Quality Engineering team in San Francisco. This hands-on role sits at the intersection of hardware and data, focusing on understanding product performance and identifying improvement opportunities.
You will analyze manufacturing, factory test, device telemetry, field, warranty, and failure analysis data to identify quality trends and emerging issues. Key responsibilities include connecting data across manufacturing systems, test logs, and field returns to understand relationships between how products are built, tested, and perform in the field. You'll support failure investigations by identifying patterns and correlations that connect field failures back to manufacturing processes, components, or test results.
The role involves comparing failed and known-good populations to identify signals associated with downstream failures, analyzing manufacturing and final test parameters to improve screening and escape detection, and building cohort-based warranty and field-quality analysis across product, build, factory, component, and time in field. You'll apply statistical methods to separate meaningful signals from normal variation and help teams make data-driven quality decisions.
You will develop monitoring and early-warning indicators for emerging quality issues, partner with Quality and Engineering teams to validate findings through failure analysis and process experiments, and identify gaps in manufacturing and quality data. Building scalable analytics, dashboards, and automated reporting is essential to give engineering teams clear visibility into product and manufacturing quality. You'll also partner with Data Engineering and Data Science teams when new data pipelines or infrastructure are needed while owning Hardware Quality use cases.
Required qualifications include 5+ years of experience working with data in engineering, manufacturing, quality, reliability, operations, or related technical environments. You need strong SQL skills and hands-on Python experience for data analysis and automation, experience with large datasets, and the ability to turn ambiguous engineering questions into structured analysis.
