Data Analytics Specialist - Quality Operations

General Motors•Pontiac, MI

2d•Onsite

About The Position

The role is described as a Lab Quality Engineer and recognized technical contributor and resource in Engine Dyno Testing, addressing broad and complex problems. The position guides engineering, laboratory operations, and quality teams on instrumentation, equipment, software, processes, and procedures needed for compliant testing. It validates instrumentation, diagnostics, calibrations, test procedures, and collected data; supports problem-solving and first-time quality improvement; develops efficiency tools and processes; leads quality innovation; and completes quality-system activities. The role also trains team members, maintains project Standard Work documents, reviews data and performs statistical analysis, tracks department, project, and Business Plan Deployment tasks, and maintains monthly laboratory and operations quality reports. It performs data acquisition and database management using PC software and maintains records of status and changes. The posting also describes preferred analytics work involving data pipelines and analytical workloads, predictive analytics, AI/ML, Power BI, governance, integrations, and cross-functional delivery. This is an onsite position, with the selected candidate expected to report to a specific location on a full-time basis. GM does not provide immigration-related sponsorship for this role, and the role is not eligible for relocation benefits.

Requirements

Bachelor’s degree; an engineering degree is preferred, though other fields of study combined with appropriate experience may be considered.
Red X, OPEX, and DFSS certifications.
Working knowledge of automotive vehicle systems.
Knowledge and application of statistical methods when applying data analysis.
Working knowledge of lean engineering and quality systems.
Strong critical-thinking skills.
People skills to work effectively with others, including global teams.
Strong oral and written communication skills to interface effectively with GPG leadership.

Nice To Haves

Three or more years in analytics, data engineering, database development, or predictive modeling.
Experience with Immuta, Atlan, automotive or operational settings, DFSS, and relevant certifications.
Familiarity with Tableau, Snowflake, Microsoft Fabric, AWS, Google Cloud, Collibra, and Salesforce.
Experience collecting, integrating, cleaning, reconciling, and validating data; building, troubleshooting, and optimizing production Databricks pipelines and analytical workloads using advanced SQL, Python/PySpark, Delta Lake, and large datasets; and applying testing and version control to maintain reliable code.
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, as well as practical data governance and stewardship 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.

Responsibilities

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