Senior Data Engineer - Vehicle Quality Engineering

General Motors•Austin, TX

4d•$125,000 - $168,650•Hybrid

About The Position

This role is categorized as hybrid. This means the successful candidate is expected to report to Warren Global Technical Center or Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. 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.

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, 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.

Nice To Haves

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.

Responsibilities

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.