Staff Data Engineer
AI summary of the role
Principal-level (Level 8) individual contributor data engineering role at GM, blending data engineering with data science and AI engineering to build Azure Databricks-based data products and enable AI/ML at scale.
What you’ll do
- Design, build, and productionize reliable, scalable, secure data pipelines and data products in Azure Databricks supporting AI, analytics, and operational use cases.
- Lead end-to-end transformation of raw data from heterogeneous sources into trusted, governed datasets for analytics, model development, and AI enablement.
- Define and champion architecture, design patterns, and best practices (Medallion Architecture, Delta Lake, data quality, observability) across teams.
- Lead AI and data science enablement: deliver feature-ready data, experimentation workflows, and scalable patterns for model development, deployment, and monitoring.
What you’ll bring
- Bachelor's degree in CS, Software Engineering, Data Engineering, or related field, or equivalent experience.
- 8+ years of relevant full-time experience in data engineering or closely related roles.
- Extensive hands-on experience with Databricks, Apache Spark, Delta Lake, and modern cloud data patterns for batch and streaming pipelines.
- Proficiency in Python or Scala, and SQL with performance tuning on large-scale datasets.
Technologies
Azure Databricks · Apache Spark · Delta Lake · Python · Scala · SQL · MLOps · Kafka · Event Hubs · Medallion Architecture
About General Motors
Global automaker selling Chevrolet, GMC, Cadillac, and Buick vehicles, plus financing and connected services, while shifting into EVs and driver assistance.
Public · 5000+ people
Source and classification
Internal deployment & tooling · Evidence for this classification:
reusable patterns, and influence roadmaps across multiple teams or products. What You’ll Do Design, build, and productionize reliable, scalable, and secure data pipelines and data products in Azure Databricks that support AI, analytics, and operational use cases across multiple business domains. Lead the end-to-end transformation of raw data from numerous, heterogeneous source systems into trusted, well-structured, and governed datasets suitable for downstream analytics, model development, and AI enablement. Define and champion architecture, design patterns, and best practices (e.g., Medallion Architecture, Delta Lake standards, data quality and observability) that can be adopted across teams. Drive strategic improvements in internal processes, delivery patterns, and technical solutions that support broader functional and enterprise data strategy, increasing efficiency,
More from the job description
Job Description This role is categorized as hybrid. This means the successful candidate is expected to report to GM Warren Global Technical Center or Austin Technical Center three times per week, at minimum or other frequency dictated by the business if more than 3 days. The Role This role is for a principal-level individual contributor in Data Engineering (Level 8) who leads complex, cross-team technical initiatives, sets direction for key data domains, and drives material improvements in processes, services, and delivery patterns across the organization. At this level, the individual is expected to operate with broad autonomy, define and execute on strategy within their scope, resolve highly complex and non-standard problems using advanced analytical thinking, and serve as a primary technical authority and multiplier for the broader team. The role is anchored in data engineering and includes an additional data science profile to strengthen AI data enablement, experimentation support, and close collaboration with data scientists and business partners, with an expanded AI-engineering focus to strengthen AI-ready data products, experimentation, natural-language analytics, and production AI capabilities. Data engineers at GM are expected to build and maintain reliable, scalable data infrastructure, transform raw data into high-quality datasets for analytics and advanced data [... source excerpt omitted ...] is expected to shape technical direction, establish standards and reusable patterns, and influence roadmaps across multiple teams or products. What You’ll Do Design, build, and productionize reliable, scalable, and secure data pipelines and data products in Azure Databricks that support AI, analytics, and operational use cases across multiple business domains. Lead the end-to-end transformation of raw data from numerous, heterogeneous source systems into trusted, well-structured, and governed datasets suitable for downstream analytics, model development, and AI enablement. Define and champion architecture, design patterns, and best practices (e.g., Medallion Architecture, Delt [... source excerpt omitted ...] king down work, and ensuring cohesive, high-quality delivery across contributors. Influence key engineering decisions, technology choices, and long-term roadmaps for your area of responsibility, balancing innovation with operational excellence and sustainability. Mentor and coach engineers and data scientists through deep technical guidance, code and design reviews, knowledge sharing, and strong engineering practices consistent with and extending beyond Level 7 expectations. Help evolve team culture, practices, and tooling around DevOps, DataOps, and MLOps, including CI/CD for data pipelines, testing strategies, observability, governance, and reliability. Communicate complex techn
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