Data Engineering Manager

Experteer Overview As Data Engineering Manager you will lead a team building scalable, cloud-native data products for GM’s customer data platforms. You’ll steer multi-cloud Lakehouse initiatives powering OnStar, Marketing, and AI-driven analytics, delivering trusted data assets and scalable pipelines. You’ll influence strategy, mentor engineers, and drive modernization from legacy systems to AI-ready data platforms. This role combines deep technical leadership with people management to enable cross-functional collaboration and impactful business outcomes. Compensation / Benefits * Lead and grow a high-performing data engineering team, including performance management and onboarding * Define and execute the technical roadmap for Customer, Marketing, and OnStar data products with multi-cloud interoperability * Architect and build scalable batch and streaming pipelines using Databricks, Spark, Delta Lake, and Unity Catalog * Own end-to-end delivery of data engineering initiatives from requirements to post-deploy support * Drive modernization to cloud-native, multi-cloud Lakehouse architectures * Enable AI/ML by building governed, reusable data products for predictive analytics and GenAI * Collaborate with architecture and platform teams to align standards and reusable components * Promote engineering excellence through CI/CD, IaC, observability, data quality, and automated testing * Optimize performance, scalability, reliability, and cloud cost efficiency * Collaborate with Business, Product, Marketing, Analytics, Security, and Architecture teams to achieve business outcomes and innovation * Foster a culture of innovation, continuous learning, and engineering excellence Tasks * 7+ years in Data Engineering, Software Engineering, or distributed data platforms * 3+ years leading data or software engineering teams * Deep expertise with Databricks, Apache Spark, Delta Lake, Unity Catalog, Python, SQL * Experience designing cloud-native solutions across Azure, AWS, or GCP * Strong data modeling, governance, observability, security, and engineering best practices * Experience with AI-first concepts including GenAI, RAG, vector search, and LLM apps * Experience building large-scale distributed batch and streaming data pipelines * Excellent leadership, communication, and stakeholder management Key requirements * hybrid work model * relocation benefits * career development * total rewards resources * inclusive culture * opportunities to drive innovation

platforms. to post-deploy support * Drive modernization to cloud-native, multi-cloud Lakehouse architectures * Enable AI/ML by building governed, reusable data products for predictive analytics and GenAI * Collaborate with architecture and platform teams to align standards and reusable components * Promote engineering excellence through CI/CD, IaC, observability, data quality, and automated testing * Optimize performance, scalability, reliability, and cloud cost efficiency * Collaborate with Business, Product, Marketing, Analytics, Security, and Architecture teams to achieve business outcomes and innovation * Foster a culture of innovation, continuous learning, and engineering excellence Tasks * 7+ years in Data Engineering, Software Engineering, or distributed data platforms * 3+ years leading data or software engineering teams * Deep expertise with Databricks, Apache Spark, Delta Lake, Unity Catalog, Python, SQL * Experience designing cloud-native solutions across Azure, AWS, or GCP * Strong data modeling, governance, observability, security, and engineering best practices * Experience with AI-first concepts including GenAI, RAG, vector search, and LLM apps * Experience building large-scale distributed batch and streaming data pipelines * Excellent leadership, communication, and stakeholder management Key requirements * hybrid work model * relocation benefits * career development * total rewards resources * inclusive culture * opportunities to drive innovation