Data Engineering Manager

What you'll do

• Lead, mentor, and manage a high-performing team of data engineers including hiring and career development.
• Define and execute the technical roadmap for Customer, Marketing, and OnStar digital data products.
• Architect and build scalable batch, streaming, API, and event-driven data pipelines using Databricks and Spark.
• Drive the migration of legacy platforms to cloud-native, multi-cloud Lakehouse architectures across Azure, GCP, and AWS.
• Build trusted, governed data products to support AI, Machine Learning, and Generative AI applications.
• Champion engineering excellence through CI/CD, Infrastructure as Code, data observability, and automated testing.
• Optimize platform performance, scalability, reliability, and cloud cost efficiency.
• Own end-to-end delivery of data engineering initiatives from initial product requirements to post-deployment support.

What we're looking for

• Bachelor’s degree in computer science, Engineering, Information Systems, or a 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.
• Experience with AI-first engineering concepts including Generative AI, RAG, vector search, and LLM-powered applications.
• Strong experience building large-scale distributed batch and streaming data pipelines.
• Experience with Databricks AI capabilities, streaming technologies like Kafka, or DataOps/MLOps practices (preferred).