Data Engineering Manager

Hybrid Austin, Texas, United States of America Warren, Michigan, United States of America Full time

JR-202616187

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. 7+ years of experience in Data Engineering, Software Engineering, or production grade Distributed Data Platforms. 3+ years of proven experience leading and managing data engineering or software engineering teams. Deep expertise with Databricks, Apache Spark, Delta Lake, Unity Catalog, Python, SQL, and modern Lakehouse architectures. Experience designing cloud-native solutions across Azure, AWS, or GCP. Strong understanding of data modeling, data governance, observability, security, and engineering best practices. Experience with or exposure to AI-first engineering concepts, including Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), vector search, and LLM-powered applications. Strong experience building large-scale distributed batch and streaming data pipelines. Excellent leadership, communication, and stakeholder management skills, with the ability to translate business needs into technical solutions. What Will Give You A Competitive Edge ( Preferred Qualifications) Experience with Databricks AI capabilities (MLflow, Mosaic AI, Delta Live Tables, Feature Store, Vector Search, Genie Spaces). Experience building Customer 360, Marketing, Personalization, CDP, or Connected Services platforms. Experience implementing DataOps/MLOps practices, including CI/CD pipelines and automated testing for data pipelines Experience with streaming technologies such as Kafka or Azure Event Hubs. Experience integrating with and working with data from Shopify, Heap, Adobe SDK and AppsFlyer Knowledge of Data Mesh, Data Products, and modern cloud-native architecture patterns. Knowledge of data privacy, security, and regulatory considerations for enterprise data Experience operating critical data platforms with strong SLA's and support processes. 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