Staff Data Engineer Intelligent Manufacturing

Data Engineer

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

The Intelligent Manufacturing teams are responsible for ideating, incubating, and delivering new plant data solutions for General Motors Manufacturing and our partners. We integrate with business and IT teams to develop real-time solutions that leverage plant floor data to improve decisions, plant asset maintenance, safety, and operational performance, as well as Vehicle Build Data.

As a senior level Data Engineer, you will design and build industrialized data assets and pipelines to support Business Intelligence and Advanced Analytics objectives. In this senior-level technical leadership role, you'll bring a passion for quality, efficiency, and reliability, along with proven experience leading complex data engineering initiatives from concept to production. You will work in a highly collaborative environment across databases, streaming technology, CI/CD, cloud platforms, and modern data engineering toolsto create large, complex data sets that meet both functional and non-functional business requirements.

Beyond strong data engineering skills, you should have a solid foundation in modern software engineering principlesincluding code quality, design patterns, testing, and CI/CDto deliver robust, maintainable, and production-ready systems. The ideal candidate combines a data-driven mindset with a strong understanding of business priorities, demonstrating creativity, sound decision-making, and the ability to influence and collaborate across teams.

Provide technical leadership for complex data engineering initiatives from concept through production, ensuring solutions are high-quality, efficient, and reliable.

Assemble large, complex data sets that meet both functional and non-functional business requirements.

Identify, design, and implement process improvements, including automation, data delivery optimization, and redesign for greater scalability.

Architect, build, and optimize highly scalable data pipelines that incorporate complex transformations and efficient, maintainable code.

Design and develop new source system integrations from a variety of formats including files, database extracts, and APIs.

Lead and deliver data-driven solutions across multiple languages, tools, and technologies, contributing to architecture discussions, solution design, and strategic technology adoption.

Develop solutions for delivering data that consistently meets SLA requirements and supports operational excellence.

Partner closely with operations teams to troubleshoot and resolve production issues, ensuring platform stability.

Drive engineering excellence by applying Agile methodologies, design thinking, continuous deployment, CI/CD best practices, and performance tuning strategies.

Build tooling and automation to make deployments, production monitoring, and operational support more repeatable and efficient.

Collaborate with business and technology partners, providing strategic guidance, leadership, and coaching to influence outcomes and align with enterprise goals.

Actively mentor peers and junior engineers, fostering a culture of learning, innovation, and continuous improvement, while educating colleagues on emerging industry trends and technologies.

Represent the team in executive-level forums to communicate status, risks, opportunities, and the strategic value of data engineering initiatives.

Bachelor's degree in Computer Science, Software Engineering, or related field

10+ years of experience in data engineering, including Python or Scala, SQL, and relational/non-relational storage (ETL frameworks, big data processing, NoSQL)

5+ years of experience in distributed, petabyte-scale data processing with Spark and container orchestration (Kubernetes)

Hands-on experience with real-time data streaming in Kubernetes and Kafka

Expertise in performance tuning (partitioning, clustering, caching, serialization techniques)

Proficiency with SQL, key-value datastores, and document stores

Strong CI/CD expertise and best practices

Background in data architecture and modeling for optimized consumption patterns

Proven experience developing data models and schemas for efficient storage, retrieval, and analytics, with query performance optimization

Cloud experience with at least one major platform (Azure preferred; AWS or GCP acceptable)

Strong teamwork and leadership skills to collaborate and influence across product, program, and engineering teams

Commitment to ensuring data security, privacy, and regulatory compliance