Data Analytics Specialist - Quality Operations

General Motors · Posted 2026-11-06

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At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt.


We’re turning today’s impossible into tomorrow’s standard — from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.


Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.


The Role

The Lab Quality Engineer is a recognized technical contributor and resource in Engine Dyno Testing. The position requires the ability to address the broadest and most complex problems.  


What You’ll Do

  • Provide guidance to the engineering team for the instrumentation, equipment and software required for compliant testing
  • Provide guidance and support to laboratory operations and quality teams to relative to the processes and procedures required to ensure compliant testing
  • Validate compliance of the measurement instrumentation, diagnostics and calibrations
  • Validate compliance of the test procedure and data collected for each required test or test sequence
  • Collaborate with teams to solve problems, improve FTQ
  • Collaborate with teams to develop tools and processes to improve efficiency
  • Lead innovation activities in the Quality Department
  • Complete Quality systems related activities
  • Train team members on tools and processes
  • Maintain documents related to Standard Work for projects
  • Review data and perform statistical analysis
  • Maintain status of department, projects, and Business Plan Deployment related tasks
  • Maintain quality reports related to laboratory and operations, issuing monthly reports for status
  • Perform data acquisition and database management using various PC computer software packages and keep records of status and changes
  • Refer to the employee training documents maintained and held by the supervisor for specifics as to required job training

Your Skills & Abilities (Required Qualifications)

  • Bachelors Degree - Engineering degree preferred, other fields of study combined with appropriate experience may be considered
  • Red X, OPEX, DFSS certifications
  • Working knowledge of automotive vehicle systems
  • Knowledge of and application of statistical methods if applying data analysis
  • Working knowledge of lean engineering / quality systems and strong critical thinking skills
  • People skills to work effectively with others including global teams and strong oral/written communication skills to effectively interface with GPG leadership

What Will Give You a Competitive Edge (Preferred Qualifications)

  • 3+ years in analytics, data engineering, database development, or predictive modeling; experience with Immuta, Atlan, automotive or operational settings, DFSS, and relevant certifications is a plus. Related platforms such as Tableau, Snowflake, Microsoft Fabric, AWS, Google Cloud, Collibra, and Salesforce are also valued.
  • Demonstrated experience collecting, integrating, cleaning, reconciling, and validating data, and building, troubleshooting, and optimizing production Databricks pipelines and analytical workloads with advanced SQL, Python/PySpark, Delta Lake, and large datasets; applies testing and version control to maintain reliable code.
  • Strong predictive analytics expertise, including forecasting and regression; feature preparation, method selection, assumptions, backtesting, holdout validation, leakage prevention, interpretation, and ongoing model monitoring.
  • Hands-on experience developing, evaluating, and maintaining AI/ML models and agents for data exploration, knowledge retrieval, workflow automation, and decision support, with appropriate access controls and human review.
  • Expert Power BI skills, including semantic models, DAX, Power Query, dashboards, and Copilot-enabled functionality, alongside practical data governance and stewardship experience across quality, ownership, metadata, lineage, and controlled access.
  • Ability to partner across engineering, operations, quality, and IT; translate needs into measurable analytical outcomes; troubleshoot system and data issues; build or support applications, APIs, workflow tools, and integrations for operational users; support documentation, data planning, migration, and architecture; and manage delivery in Jira Cloud.