Data Engineering Manager

About the role

Job Description

About the Role

The Data Engineering Manager will lead a team of data engineers focused on building and operating Customer data platforms supporting OnStar, Connected Services, Customer, Marketing, Personalization, AI, and Advanced Analytics.

This is a highly technical leadership role responsible for building scalable, cloud-native data products across a multi-cloud Lakehouse architecture with a strong focus on Databricks, Distributed Data Processing, near Real-Time Streaming, AI/ML, and Generative AI. If you're passionate about distributed systems, Databricks, AI, cloud platforms, and building engineering organizations that move fast and innovate continuously, we'd love to meet you.

You are a technology leader who combines deep technical expertise with strong people leadership. You thrive in solving complex engineering challenges, embrace innovation, and are passionate about building scalable, AI-ready data platforms that power millions of connected customer experiences. Your ability to influence strategy, mentor engineers, and deliver modern data solutions will shape the future of GM's customer data ecosystem.

What You'll Do

- Lead, mentor, and grow a high-performing team of Data Engineers, including performance management, career development, hiring and onboarding.

- Define and execute the technical roadmap for Customer, Marketing, and OnStar\Digital data products, using cloud-agnostic, portable architecture that enable interoperability across Azure, GCP, AWS, and other platforms while supporting GM’s long-term multi-cloud strategy.

- Architect, Design and build scalable batch, streaming, API, and event-driven data pipelines using Databricks, Apache Spark, Delta Lake, and Unity Catalog.

- Own end-to-end delivery of data engineering initiatives from product requirements to operational support post deploy.

- Drive modernization from legacy platforms to cloud-native, multi-cloud Lakehouse architectures.

- Enable AI and Machine Learning by building trusted, reusable, and governed data products supporting predictive analytics, GenAI, and LLM applications.

- Collaborate with architecture and platform teams to align reference architectures, standards, and reusable components.

- Champion engineering excellence through CI/CD, Infrastructure as Code, Data Observability, automated testing, metadata management, and data quality.

- Optimize platform performance, scalability, reliability, and cloud cost efficiency.

- Collaborate with Business, Product, Marketing, Analytics, Security, and Architecture teams to deliver business outcomes and technical innovation.

- Foster a culture of innovation, continuous learning, experimentation, and engineering excellence.

Your Skills & Abilities (Required Qualifications)

- Bachelor’s degree in computer science, Engineering, Information Systems, or related field.