Data Quality Engineer

Delart•Menlo Park, CA

5d•$125,000 - $170,000

About The Position

Delart is hiring a Data Quality Engineer to safeguard benchmark data integrity in continuous, large-scale device testing operations. The role combines data analysis, Python-based test automation, and operational ownership to ensure benchmark datasets are healthy, reliable, and useful to product engineering teams. The engineer will support testing across multiple device form factors, work with scenario and platform developers, and collaborate with lab operations, networking, and product teams.

Requirements

Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related technical discipline.
At least 5 years of hands-on experience in data quality engineering, QA automation, test engineering, data analysis, or similar roles.
Experience validating large datasets and identifying anomalies and regressions with statistical techniques, and 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.
Experience developing and operating automated data-quality checks, dashboards, alerting systems, or analytical reporting.
Experience with CI/CD-driven test infrastructure or modern data pipeline environments, including monitoring stability and resolving failures.
Strong troubleshooting ability and experience isolating root causes across software, configuration, environment, and hardware domains.
Excellent written and verbal communication, particularly for technical reporting and communicating quality risks to engineering and product stakeholders.

Nice To Haves

Master’s degree in Data Science, Statistics, Computer Science, Electrical Engineering, or a closely related field.
Hands-on experience with device benchmarking or quality-of-experience measurement platforms, including video-result validation workflows.
Familiarity with mobile test automation frameworks such as Appium and a 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.
A demonstrated history of mentoring junior engineers and championing data-quality best practices.

Responsibilities

Define and implement data-quality checks, acceptance criteria, and validation plans based on benchmarking requirements and product-team expectations.
Conduct daily operational reviews of more than 50 devices in continuous test automation; investigate anomalies, debug automation issues, and address lab-environment variances.
Validate large-scale benchmark datasets and use statistical analysis to identify regressions, anomalies, and misaligned results.
Develop, deploy, and maintain automated data-quality checks, alerting systems, and interactive dashboards for data integrity, device health, and operational trends.
Generate, review, and communicate analytical reports on benchmarking outputs, quality risks, and actionable findings.
Build and support Python-based automation and operational tools for continuous benchmarking and data validation across multiple device form factors.
Collaborate directly with scenario and platform developers.
Conduct in-depth investigations and monthly analytics to determine the root causes of anomalous data and distinguish genuine device performance from automation artifacts.
Review video-capture logs from test runs to validate outcomes and troubleshoot scenario failures.
Debug data discrepancies by triaging lab configuration, application settings, wireless conditions, and device defects, and escalate issues to the appropriate owners.
Operate and monitor CI/CD-driven test environments, maintain stability, and resolve operational failures.
Document validation protocols, triage guides, and reporting workflows to support operations at scale.
Collaborate with lab operations, networking, and product teams to resolve environment and device issues and establish data-quality standards and practices.