Senior Data Engineer - Vehicle Quality Engineering

Build and operationalize data science solutions that detect and prevent emerging vehicle, software, and product-quality issues.

Responsibilities

• Partner with subject matter experts to define metrics, analytical objectives, thresholds, and decision criteria.
• Perform comprehensive, unbiased analysis to surface trends, anomalies, relationships, and emerging risks across vehicle, software, warranty, diagnostics, fleet, and product-quality data.
• Design, test, and deploy anomaly-detection and pre-emptive monitoring algorithms for issues that impact customers.
• Build end-to-end analytical pipelines that convert raw data into reliable insights, dashboards, alerts, and operational workflows.
• Create alerting solutions that enable internal customers to respond quickly to high-priority vehicle and product issues.
• Establish validation approaches using historical data, controlled testing, domain expertise, and (when appropriate) real-world observations.
• Communicate findings, assumptions, limitations, and recommendations to technical and non-technical audiences.
• Improve performance, scalability, reliability, and cost efficiency through model, query, pipeline, and architecture improvements.
• Standardize analytical processes and adopt modern Azure-based technologies to reduce complexity and redundant data handling.
• Support vehicle and software launches by delivering readiness insights and analytical tools.
• Document analytical methods, data lineage, operating procedures, and known limitations for maintainability and adoption across teams.
• Deliver committed critical analytics features with clear acceptance criteria and defined production support plans.
• Launch or enhance pre-emptive monitors to detect emerging concerns earlier than reactive processes.
• Provide trusted dashboards and alerts to support real-time, data-driven decisions for engineering, quality, warranty, and launch teams.
• Improve existing analytics products for performance, scalability, usability, and cost.
• Demonstrate measurable customer value through reduced manual effort, faster issue investigation, improved launch readiness, and earlier risk mitigation.
• Increase adoption by working with software domains and operational teams on how insights are used and where capabilities should expand.
• Strengthen analytical governance using documented definitions, validation methods, data quality checks, and repeatable operating practices.
• Select analytical methods aligned to the business question, data constraints, and decision context.
• Test assumptions and validate results with sound statistical and domain-informed practices.
• Balance detection sensitivity, false positives, explainability, and operational usefulness.
• Design solutions around how internal customers make decisions and use outputs.
• Own outcomes across discovery, development, deployment, support, and ongoing improvement.
• Troubleshoot issues quickly and follow assigned work through completion.
• Design solutions to be maintainable, scalable, secure, and cost-conscious.
• Explore practical new methods and technologies when existing approaches do not solve the problem adequately.
• Identify opportunities to automate manual processes and improve performance.
• Take educated risks using validation and safeguards to minimize downside.
• Work effectively with engineering, quality, warranty, analytics, and business partners; listen actively and build productive relationships.
• Mentor others and contribute reusable knowledge to increase impact across the analytics organization.
• Build algorithms and dashboards/alerts to summarize suspect behavior and support engineering investigation of problematic charging conditions or stations.
• 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 a related field, or equivalent practical experience.
• 7+ years of data engineering/development or related experience.
• Experience applying statistical analysis and/or machine learning approaches (including anomaly detection, forecasting, classification, or related methods) to real-world business or engineering problems.
• Strong Python skills for data analysis, algorithm development, automation, and production-oriented data workflows.
• Experience with SQL and modern data technologies to query, transform, validate, and analyze large and complex datasets.
• Ability to take an analytical solution from problem definition through testing, deployment, monitoring, and continuous improvement.
• Experience communicating results and recommendations to stakeholders with varying technical expertise.
• 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 & 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

Hybrid Work Location

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

Compensation

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

Immigration Sponsorship

• 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
• Reduction in time required 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
• Demonstration of GM behaviors: Own the Outcome, Think Customer, Innovate Now, Win with Integrity, Commit to Customers, and 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 methods can improve detection quality while controlling false positives and operational burden
• Experience mentoring peers, sharing technical best practices, or leading work across organizational boundaries