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Data Engineering Manager

  • 위치
    • Austin, Texas
    • Warren, Michigan
  • 직무 유형 Full time
  • 게시됨
  • Job Requisition JR-202616187

설명

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.

GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc.)

이 직무는 하이브리드 직무로 분류됩니다. 즉, 선발된 지원자는 특정 근무지로 주 3일 이상(또는 관리자가 지정한 다른 빈도로) 특정 근무지로 출근해야 합니다.

이 직무는 리로케이션 혜택을 받을 수 있습니다.

다양성 정보

General Motors는 법적으로 금지된 차별을 배제하는 것은 물론 포용성과 소속감을 진정으로 장려하는 직장이 되기 위해 노력하고 있습니다. 당사는 다양성이 보장되는 환경에서 직원들이 역량을 발휘하고 우리 고객을 위한 더 좋은 제품을 개발할 수 있다고 믿습니다. 따라서 입사에 관심 있는 사람이 있다면 포지션별 주요 업무와 자격을 확인하고 본인이 보유한 기술과 능력에 부합하는 모든 포지션에 적극적으로 지원하기를 장려합니다. 지원자는 채용 과정에서 역할 관련 평가(해당하는 경우) 및/또는 채용 전 스크리닝을 통과해야 합니다.  자세한 정보는 GM 채용 과정 안내를 참고하십시오.

공평한 취업 기회 선언 (미국)

General Motors는 공평한 기회를 제공하는 고용주임을 자부합니다.  자격을 만족하는 지원자는 인종과 피부색, 성별, 성적 지향, 성별 정체성, 국적, 장애, 재향 군인 보호법 적용 여부와 상관없이 채용 후보로서 심사를 받습니다. 

숙소 (미국 및 캐나다)

General Motors는 장애인을 포함한 모든 구직자들에게 취업 기회를 제공합니다. 구직이나 취업 지원에 도움이 되는 합리적인 숙소가 필요한 경우 [email protected]으로 이메일을 보내시거나 800-865-7580으로 전화주십시오. 이메일에, 귀하가 요청하는 특정한 숙소에 대한 설명과 귀하가 지원하는 직무와 채용 요청서 번호를 포함해주세요.