Job Description
Sponsorship: GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE. DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP (e.g., H-1B, TN, STEM OPT, etc.) NOW OR IN THE FUTURE.
Work Arrangement:
This role is categorized as hybrid. This means the successful candidate is expected to report to the office four times per week or other frequency dictated by the business.
The Role
Global Manufacturing Engineering at GMTCI is a bridge between Global Manufacturing Plants & Global Product Engineering in getting the Product & Process Right. GMTCI’s strategy is to “Execute Design / Simulation / Virtual / Engineering Workshare for the Globe + Enable Factory for the Future”
This position of Manufacturing Data Analytics Engineer is a Mfg 4.0 initiative in ME Vehicle Systems Automation Group which is primarily responsible for applying Data Analytics techniques on GM manufacturing processes in GM manufacturing plants across the globe to solve complex manufacturing business problems with big data. In this position, the engineer supports critical business operations and decision making through creation domain data exploration, data processing pipeline, and business intelligence reports.
This is primarily a non-travel job. Business travel will be necessary on special requirements and will be for a short duration. Depending on the need, there will be an International Short-Term assignment for 6 months in the first year for training & development.
What You’ll Do
- Design, develop, and maintain full-stack manufacturing intelligence applications using modern frontend, backend, and database technologies
- Build intuitive React/TypeScript user experiences for manufacturing dashboards, workflow tools, operational insights, and engineering applications
- Develop scalable backend services and APIs using Python and FastAPI or comparable frameworks, with clear service boundaries, resilient error handling, and efficient request processing
- Integrate applications with manufacturing and enterprise systems, including MES, SCADA, PLC/controls data, historians, equipment interfaces, and event-driven data services
- Design and optimize PostgreSQL data models, queries, and data pipelines for production, quality, equipment, and process information
- Implement secure authentication, authorization, auditability, and data-access controls for applications used across GM manufacturing and engineering environments
- Deploy and operate applications across enterprise, cloud, and edge environments using Docker, Kubernetes, CI/CD automation, and production monitoring
- Create automated unit, integration, and end-to-end tests and establish engineering practices that improve application reliability, maintainability, and release quality
- Translate manufacturing and engineering problems into clear technical designs, user stories, acceptance criteria, and measurable product outcomes
- Collaborate with controls, manufacturing, quality, data science, cybersecurity, and IT teams to deliver solutions that improve visibility, productivity, quality, and decision-making
- Troubleshoot production issues, perform root-cause analysis, and implement performance and reliability improvements across the application stack
- Document architecture, APIs, deployment procedures, operating standards, and support processes while contributing to a culture of continuous improvement.
Your Skills & Abilities (Required Qualifications)
- Bachelor's or higher degree in Computer Science, Software Engineering, Electrical Engineering, Computer Engineering, or equivalent experience
- Ability to work effectively in both central engineering and plant environments, including time on the plant floor around operating equipment
- 5+ years of professional software engineering experience building and supporting production full-stack applications
- 3+ years of experience delivering manufacturing intelligence, industrial software, or data-intensive enterprise applications across frontend, backend, and data layers
- Advanced TypeScript/JavaScript skills with React, including component architecture, state management, data visualization, responsive and accessible user interfaces, and frontend testing
- Strong Python development skills with FastAPI or comparable frameworks, including asynchronous programming, REST/OpenAPI service design, request validation, dependency management, and automated testing
- Advanced SQL and PostgreSQL skills, including relational data modeling, schema design, migrations, indexing, query and transaction performance, and data-quality controls
- Experience delivering manufacturing intelligence, industrial software, or data-intensive enterprise applications that connect operational data to business and engineering workflows
- Experience designing data pipelines and event-driven architectures for equipment telemetry, time-series data, production data, quality data, and manufacturing workflows
- Hands-on experience with Git, Docker, Kubernetes, CI/CD automation, environment configuration, and deployment practices for production applications
- Demonstrated intermediate-level proficiency with Microsoft Azure services and cloud-based application development.
- Experience implementing automated unit, integration, contract, and end-to-end testing, as well as observability using structured logging, metrics, tracing, health checks, and alerting
- Working knowledge of application and platform security, including OAuth/OIDC, role-based access control, secrets management, secure coding, vulnerability remediation, and least-privilege design
- Demonstrated ability to troubleshoot distributed systems, diagnose performance and reliability issues, and translate manufacturing requirements into maintainable technical designs and production releases
- Basic understanding of Databricks and its integration with other systems
- Working understanding of automotive manufacturing processes
- Working knowledge of applying AI/ML techniques to manufacturing data, including integrating AI services and models into production applications, evaluating model outputs, using prompt engineering or retrieval-augmented generation (RAG), and monitoring AI system performance
What Will Give You a Competitive Edge (Preferred Qualifications)
- Intermediate-to-expert proficiency with Databricks, including Spark and SQL data pipelines, Delta Lake, workflow orchestration, data-quality controls, and integration with manufacturing and enterprise systems
- Experience integrating OT and IT systems such as MES, SCADA, PLC/controls systems, OPC UA, MQTT, REST APIs, historians, and event or message-based platforms
- Experience working with manufacturing data models, time-series data, equipment telemetry, production genealogy, quality data, or process data
- Experience building data visualization and analytical user experiences for operational, engineering, quality, or maintenance users
- Experience with cloud-native and edge architectures, including Kubernetes, infrastructure as code, distributed systems, and disconnected or intermittently connected environments
- Knowledge of GM's controls standards, manufacturing technology standards, or comparable industrial automation and software integration standards
- Experience with event-driven architectures, streaming platforms, message queues, data historians, or real-time data processing
- Experience implementing observability practices, including structured logging, metrics, distributed tracing, alerting, and service-level objectives
- Experience with automated security scanning, software supply-chain controls, vulnerability remediation, and cybersecurity practices for operational technology environments
- Experience leading technical design discussions, mentoring engineers, reviewing code, and establishing reusable software patterns across teams
- Strong collaborative skills and the ability to communicate effectively with technical and non-technical stakeholders in a fast-paced manufacturing environment





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