Senior Data Engineer - Vehicle Quality Engineering

The Senior Data Scientist will develop and operationalize data science solutions that identify emerging vehicle, software, and product-quality issues before they become larger customer or launch risks. This role partners closely with Product Quality, Warranty, Vehicle Engineering, software teams, and other subject matter experts to translate complex operational questions into trustworthy metrics, detection algorithms, dashboards, and alerting workflows.

The successful candidate will combine strong statistical and analytical judgment with the ability to build production-ready data products. They will work across the full lifecycle: understanding the customer problem, validating the data, developing and testing an analytical approach, deploying the solution, monitoring its performance, and continuously improving adoption and scalability.

• →Partner with subject matter experts to define meaningful metrics, analytical objectives, thresholds, and decision criteria.
• →Conduct comprehensive, unbiased analysis to identify trends, anomalies, relationships, and emerging risks in vehicle, software, warranty, diagnostic, fleet, and product-quality data.
• →Design, test, and deploy anomaly-detection and pre-emptive-monitoring algorithms for customer-impacting issues.
• →Build end-to-end analytical pipelines that transform raw data into reliable insights, dashboards, alerts, and operational workflows.
• →Create customer-focused visualizations that allow engineering and quality teams to investigate fleet, VIN, and software-version,
• →Develop alerting solutions that help internal customers 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 clearly to technical and non-technical audiences.
• →Improve solution performance, scalability, reliability, and cost efficiency through model, query, pipeline, and architecture improvements.
• →Reduce complexity and redundant data handling by standardizing analytical processes and adopting modern Azure-based technologies.
• →Support vehicle and software launches by delivering readiness insights and analytical tools.
• →Document analytical methods, data lineage, operating procedures, and known limitations so solutions can be maintained and adopted across teams.
• →Contribute reusable patterns, best practices, and mentoring that increase the impact of the broader analytics organization.

• Deliver committed critical analytics features for internal customers with clear acceptance criteria and production support plans.
• Launch or improve pre-emptive monitors that identify emerging vehicle, software, or product-quality concerns earlier than traditional reactive processes.
• Provide trusted dashboards and alerts that enable real-time, data-driven decisions for engineering, quality, warranty, and launch teams.
• Improve the performance, scalability, usability, and cost profile of existing analytics products.
• Demonstrate measurable customer value through reduced manual effort, faster issue investigation, improved launch readiness, or earlier risk mitigation.
• Increase adoption by working directly with software domains and operational teams to understand how insights are used and where capabilities should expand.
• Strengthen analytical governance through documented definitions, validation methods, data quality checks, and repeatable operating practices.
• Selects appropriate analytical methods based on the business question, data limitations, and decision context.
• Tests assumptions and validates results using sound statistical and domain-informed practices.
• Balances detection sensitivity, false positives, explainability, and operational usefulness.
• Starts with how internal customers make decisions and designs the solution around that workflow.
• Converts complex data into clear, actionable insights.
• Builds trust through responsiveness, follow-through, transparency, and data quality.
• Owns outcomes across discovery, development, deployment, support, and improvement.
• Troubleshoots issues quickly and follows assigned work through completion.
• Designs solutions that are maintainable, scalable, secure, and cost-conscious.
• Explores practical new methods and technologies when existing approaches do not adequately solve the problem.
• Identifies opportunities to automate manual processes and improve performance.
• Takes educated risks while using validation and safeguards to minimize downside.
• Works effectively with engineering, quality, warranty, analytics, and business partners.
• Listens actively, shares information, and builds productive relationships.
• Mentors others and contributes reusable knowledge to the organization.

• Develop pre-emptive detection for emerging product-quality issues and other customer-impacting patterns.
• Build algorithms that summarize suspect behavior and help engineering teams identify problematic charging conditions or stations.
• Create dashboards and alerts that provide timely visibility into 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.

Success will be evaluated through a combination of technical quality, customer impact, and organizational contribution, including:

• 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.

• 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, machine learning, anomaly detection, forecasting, classification, or related data-science methods to real-world business or engineering problems.
• Strong Python skills for data analysis, algorithm development, automation, and production-oriented data workflows.
• Experience querying, transforming, validating, and analyzing large and complex datasets using SQL and modern data technologies.
• Demonstrated ability to take an analytical solution from problem definition through testing, deployment, monitoring, and continuous improvement.
• Experience communicating analytical results and recommendations to stakeholders with different levels of technical expertise.
• Strong data-quality mindset, including the ability to investigate discrepancies, assess assumptions, and explain limitations.
• Demonstrated ownership, sound judgment, responsiveness, and ability to deliver work to completion in a cross-functional environment.

• 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.

This job may be eligible for relocation benefits.

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