Overview

Join our team as a Data Quality Engineer, where you'll be the guardian of benchmark data integrity supporting continuous large-scale device testing operations. This hands-on role combines advanced data analysis, Python-based test automation, and operational ownership, ensuring the health, reliability, and actionable insights of benchmark datasets used by product engineering teams.

Responsibilities

• Define and implement data-quality checks, acceptance criteria, and validation plans grounded in benchmarking requirements and product-team expectations.
• Run daily operational reviews of 50+ devices under continuous test automation—investigating anomalies, debugging automation issues, and addressing lab-environment variances.
• Systematically validate large-scale benchmark datasets, leveraging statistical analysis to identify regressions, anomalies, and misaligned results.
• Develop, deploy, and maintain automated data-quality checks, alerting systems, and interactive dashboards to track data integrity, device health, and operational trends.
• Generate, review, and communicate clear analytical reports on benchmarking outputs, highlighting quality risks and actionable findings for stakeholders.
• Build and support Python-based automation and operational tooling for continuous benchmarking and data validation across multiple device form factors, collaborating directly with scenario and platform developers.
• Conduct in-depth investigations and monthly analytics to diagnose the root cause of anomalous data, distinguishing genuine device performance from automation artifacts.
• Review video capture logs from test runs to validate outcomes and troubleshoot scenario failures.
• Methodically debug data discrepancies, triaging systematically across lab configuration, application settings, wireless conditions, and device defects, and escalate issues to the proper owners.
• Operate and monitor CI/CD-driven test environments, ensuring stability and rapidly resolving operational failures.
• Document validation protocols, triage guides, and reporting workflows to facilitate operation at scale.
• Collaborate closely with lab operations, networking, and product teams to address environment and device issues, and help establish robust data-quality standards and practices.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related technical discipline.
• 5+ years of hands-on experience in data quality engineering, QA automation, test engineering, data analysis, or similar roles.
• Demonstrated expertise in validating large datasets, identifying anomalies and regressions with statistical techniques, and clearly communicating findings to stakeholders.
• Proficiency with Python for automation, test scripting, and advanced data analysis (including debugging and maintaining codebases).
• Solid knowledge of SQL for querying, inspecting, and validating measurement and test-run data.
• Prior experience developing and operating automated data-quality checks, dashboards, alerting, or analytical reporting.
• Experience working with CI/CD-driven test infrastructures or modern data pipeline environments, monitoring system stability and resolving failures.
• Strong troubleshooting ability, with a record of isolating root causes across software, configuration, environment, and hardware domains.
• Excellent written and verbal communication, particularly for technical reporting and conveying quality risks to engineering and product stakeholders.

Preferred Qualifications

• Master’s degree in Data Science, Statistics, Computer Science, Electrical Engineering, or closely related field.
• Hands-on experience with device benchmarking or quality-of-experience measurement platforms, including workflows involving video result validation.
• Familiarity with mobile test automation frameworks (e.g., Appium) and deep understanding of mobile application behavior.
• Experience diagnosing environmental, RF, or hardware-related variables in device testing labs.
• Operational experience managing device fleets, provisioning, and supporting large-scale testbeds.
• Demonstrated history of mentoring junior engineers and championing best practices for data quality.