Senior Data Analytics Engineer, Hardware Quality

Our mission at the company is to empower every person to own their inner potential. Our award-winning products help our global community gain a deeper knowledge of their readiness, activity, and sleep quality by using their the company Ring and its connected app. We've helped millions of people understand and improve their health by providing daily insights and practical steps to inspire healthy lifestyles.

Empowering the world starts with living our values and empowering our team. As a quickly growing company focused on helping people live healthier and happier lives, we ensure that our team members have what they need to do their best work — both in and out of the office.

We are looking for a Senior Quality Data Engineer – Hardware to join our Hardware Quality Engineering team.

In this role, you will bring together manufacturing, test, device telemetry, field, warranty, and failure analysis data to understand how our products are performing and where we can improve. You will work closely with engineering and manufacturing teams to identify quality trends, investigate failures, improve detection, and help prevent known issues from reaching customers.

This is a hands-on role for someone who enjoys working at the intersection of hardware and data. You will work closely with Hardware Engineering, Quality, Manufacturing, Reliability, Firmware, Operations, and Data teams.

• Analyze manufacturing, factory test, device telemetry, field, warranty, and failure analysis data to identify quality trends and emerging issues.

• Connect data across manufacturing systems, test logs, device telemetry, and field returns to understand relationships between how a product was built, how it performed during test, and how it performs in the field.

• Support failure investigations by identifying patterns and correlations that help connect field failures back to manufacturing processes, components, test results, or product behavior.

• Compare failed and known-good populations to identify manufacturing, test, telemetry, or component signals associated with downstream failures.

• Analyze manufacturing and final test parameters to identify marginal passes, abnormal trends, and opportunities to improve screening and escape detection.

• Build cohort-based warranty and field-quality analysis across product, build, factory, component, configuration, and time in field.

• Apply statistical methods to separate meaningful product and process signals from normal variation and help teams make data-driven quality decisions.

• Develop monitoring and early-warning indicators that help identify emerging quality issues before they become larger field or warranty problems.

• Partner with Quality and Engineering teams to validate findings through failure analysis, controlled builds, additional inspection, or process experiments, and measure whether corrective actions are working.

• Identify gaps in manufacturing and quality data, including missing data, inconsistent definitions, traceability gaps, or conflicting metrics, and work with the appropriate teams to resolve them.

• Build scalable analytics, dashboards, and automated reporting that give engineering teams clear visibility into product and manufacturing quality.

• Partner with Data Engineering and Data Science teams when new data pipelines or infrastructure are needed while owning the Hardware Quality use cases and analysis.

• Communicate findings clearly and turn complex datasets into conclusions and recommendations that engineering teams and leadership can act on.

• 5+ years of experience working with data in engineering, manufacturing, quality, reliability, operations, or a related technical environment.

• Strong SQL skills and hands‑on experience with Python for data analysis and automation.

• Experience working with large datasets and turning ambiguous engineering or product questions into structured analysis.

• Experience with manufacturing, hardware test, reliability, warranty, field, or product data.

• Working knowledge of statistics and experience comparing populations, identifying correlations, and evaluating trends.

• Experience building analytics and visualizations using Tableau, Databricks, or similar tools.

• Strong problem‑solving skills and curiosity to understand why a product or process is behaving the way it is.

• Ability to work effectively across Hardware, Manufacturing, Firmware, Quality, Reliability, and Data teams.

• Ability to communicate technical findings clearly to both…