Senior Data Engineer - Vehicle Quality Engineering

Drive production-grade data science and anomaly detection to improve vehicle, software, and product-quality outcomes.

Responsibilities

• Partner with subject matter experts to define metrics, analytical goals, thresholds, and decision criteria
• Perform unbiased analysis on vehicle, software, warranty, diagnostic, fleet, and product-quality data to find trends, anomalies, relationships, and emerging risks
• Design, test, and deploy anomaly-detection and pre-emptive monitoring algorithms for customer-impacting issues
• Build end-to-end analytical pipelines that convert raw data into reliable insights, dashboards, alerts, and operational workflows
• Create alerting solutions that enable internal teams to respond quickly to high-priority vehicle and product issues
• Establish validation approaches using historical data, controlled testing, domain expertise, and real-world observations when appropriate
• Communicate findings, assumptions, limitations, and recommendations to technical and non-technical audiences
• Improve model, query, pipeline, and architecture performance for scalability, reliability, and cost efficiency
• Reduce complexity and redundant data handling by standardizing analytical processes and adopting modern Azure-based technologies
• Support vehicle and software launches with readiness insights and analytical tools
• Document analytical methods, data lineage, operating procedures, and known limitations to support maintenance and cross-team adoption
• Deliver reusable patterns and best practices, including mentoring that increases broader analytics impact
• Ship committed critical analytics features with clear acceptance criteria and production support plans
• Launch or enhance pre-emptive monitors to identify emerging concerns earlier than reactive processes
• Provide trusted dashboards and alerts for engineering, quality, warranty, and launch decision-making
• Improve the performance, scalability, usability, and cost profile of existing analytics products
• Demonstrate measurable customer value such as reduced manual effort, faster investigation, improved launch readiness, or earlier risk mitigation
• Increase adoption by collaborating with software domains and operational teams on how insights are used and where capabilities should expand
• Strengthen analytical governance through documented definitions, validation methods, data quality checks, and repeatable operating practices
• Select analytical methods based on business questions, data limitations, and decision context; test assumptions using statistical and domain-informed practices
• Balance detection sensitivity, false positives, explainability, and operational usability
• Start with internal decision workflows and design solutions around those operational processes
• Own outcomes across discovery, development, deployment, support, and continuous improvement
• Troubleshoot issues quickly and follow assigned work through completion
• Design maintainable, scalable, secure, and cost-conscious solutions
• Explore practical new methods and technologies when existing approaches are insufficient; identify automation opportunities
• Use validation and safeguards to minimize downside while taking educated risks
• Collaborate effectively across engineering, quality, warranty, analytics, and business partners; listen actively and share information
• Mentor others and contribute reusable knowledge
• Develop pre-emptive detection for emerging product-quality issues and other customer-impacting patterns
• Build algorithms to summarize suspect behavior and help teams identify problematic charging conditions or stations
• Create dashboards and alerts for VIP vehicle concerns, launch readiness, fleet behavior, and production risks
• Improve ingestion, query, and dashboard performance while reducing manual preparation and cloud-resource costs
• Integrate additional signals including warranty, diagnostic, software, and operational data to improve detection confidence and business context

Requirements

• Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or related field, or equivalent practical experience
• 7+ years of data engineering/development or related experience
• Experience applying statistical analysis and data-science methods such as machine learning, anomaly detection, forecasting, or classification to real-world business or engineering problems
• Strong Python skills for data analysis, algorithm development, automation, and production-oriented data workflows
• Experience using SQL and modern data technologies to query, transform, validate, and analyze large and complex datasets
• Proven ability to take an analytical solution from problem definition through testing, deployment, monitoring, and continuous improvement
• Ability to communicate analytical results and recommendations to stakeholders with varying technical depth
• Strong data-quality mindset, including investigating discrepancies, assessing assumptions, and explaining limitations
• Demonstrated ownership, sound judgment, responsiveness, and ability to deliver to completion in a cross-functional environment

Technologies

• Python
• SQL
• Azure
• Data Factory
• Databricks
• Git-based workflows
• CI/CD
• Databricks Asset Bundles

Benefits

• Health and wellbeing benefit programs including medical, dental, vision
• Health Savings Account
• Flexible Spending Accounts
• Retirement savings plan
• Sickness and accident benefits
• Life insurance
• Paid vacation and holidays
• Tuition assistance programs
• Employee assistance program
• GM vehicle discounts
• Bonus Potential: incentive pay program with payouts based on company performance, job level, and individual performance

Location and Work Model

• Warren, MI (hybrid)
• Expected to report to Warren Global Technical Center or Austin Technical Center three times per week (or other frequency dictated by the business if more than 3 days)

Compensation

• Expected base compensation: USD 125,000 - $168,650 per year
• Bonus Potential: incentive pay program based on company performance, job level, and individual performance

Immigration

• GM does not provide immigration-related sponsorship for this role
• Do not apply if you will need GM immigration sponsorship now or in the future

Measures of Success

• Accuracy, precision, recall, timeliness, and operational usefulness of deployed detection solutions
• Adoption and satisfaction among engineering, quality, warranty, and launch-readiness customers
• Reduced time to investigate issues, prepare meetings, extract data, or respond to high-priority risks
• Reliability, performance, scalability, maintainability, and cost efficiency of production analytics products
• Completion of committed critical features and effective production support
• Quality of documentation, data definitions, validation practices, and knowledge sharing
• GM behaviors: Own the Outcome, Think Customer, Innovate Now, Win with Integrity, Commit to Customers, One Team

Preferred Qualifications

• Experience with Azure data services, Data Factory, Databricks, data pipelines, workflow orchestration, or comparable cloud technologies
• Experience building operational dashboards, alerting tools, or decision-support products for engineering, quality, warranty, manufacturing, fleet, or customer-facing teams
• Experience working with vehicle, telematics, diagnostic, warranty, software-version, or product-launch data
• Experience with time-series data, event data, fleet-level analysis, VIN-level investigation, or software-quality monitoring
• Experience with modern deployment practices such as Git-based workflows, CI/CD, or Databricks Asset Bundles
• Familiarity with data modeling, data lineage, reusable analytical patterns, and scalable solution design
• Experience evaluating whether AI/ML can improve detection quality while controlling false positives and operational burden
• Experience mentoring peers, sharing technical best practices, or leading work across organizational boundaries