Senior Data Engineer – Vehicle Quality & Anomaly Detection

General Motors Corporation; Michigan; Hybrid; USD 125,000 - 169,000; Full time

Benefits offered by this job

Health benefits

Wellbeing programs

GM vehicle discounts

Job summary

General Motors Corporation seeks a Senior Data Engineer in Vehicle Quality Engineering to design and operationalize data science solutions that detect emerging vehicle and software quality issues. You will partner with Quality and Engineering teams to translate problems into reliable metrics, dashboards, and alerting workflows.

The role requires building end-to-end pipelines, validating data, deploying models, and monitoring performance, with a focus on scale, cost efficiency, and production

Qualifications

• 7+ years of data engineering/development experience.
• Experience applying statistical analysis, ML, anomaly detection, or forecasting to real-world problems.
• Strong Python skills for data workflows and production-ready pipelines.
• SQL querying, transforming, validating, and analyzing large datasets.
• Ability to take analytical solutions from discovery to deployment and monitoring.

Responsibilities

• Define metrics, objectives, thresholds, and decision criteria with SMEs.
• Identify trends, anomalies, relationships, and emerging risks in quality data.
• Design, test, and deploy anomaly-detection and monitoring algorithms.
• Build end-to-end analytical pipelines transforming data into dashboards, alerts, and workflows.
• Communicate findings clearly to technical and non-technical audiences.

Skills

Python

SQL

Data analysis

Machine learning

Big data

Education

Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or related field

Tools

Azure Data Factory

Databricks

CI/CD

Job description

General Motors Corporation seeks a Senior Data Engineer in Vehicle Quality Engineering to design and operationalize data science solutions that detect emerging vehicle and software quality issues. You will partner with Quality and Engineering teams to translate problems into reliable metrics, dashboards, and alerting workflows.

The role requires building end-to-end pipelines, validating data, deploying models, and monitoring performance, with a focus on scale, cost efficiency, and production