Staff Data Engineer

General Motors•Austin, MI

3d•$160,200 - $211,950•Hybrid

About The Position

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 science use cases, and partner closely with data scientists, analysts, software engineers, and business teams. Data scientists are expected to apply analytical and machine learning techniques, explore and prepare data, validate models, design experiments, and translate findings into actionable recommendations. The engineer is expected to shape technical direction, establish standards and reusable patterns, and influence roadmaps across multiple teams or products. This role will help establish the data and AI foundation for trusted, scalable, and reusable intelligence across GM. By combining strong data engineering with production AI capabilities, you will help teams move from fragmented data and exploratory analysis to governed data products, reliable AI experiences, and faster, better-informed decisions.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or related field, or equivalent experience.
8+ years of relevant full-time experience in data engineering or closely related roles; or equivalent depth of knowledge.
Strong, hands-on experience in data engineering, including: End-to-end pipeline development (ingestion, transformation, orchestration, monitoring).
Data modeling (batch and streaming), data integration, and production support for enterprise data platforms.
Building and operating highly reliable, scalable data products in production environments.
Extensive experience designing and optimizing batch and streaming data pipelines using Databricks, Apache Spark, Delta Lake, and modern cloud data patterns.
Proven experience supporting AI or machine-learning use cases through high-quality data preparation, feature-ready datasets, experimentation workflows, and model-development enablement.
Proficiency in: Python or Scala.
SQL, including performance tuning and working with large-scale datasets.
Relational and non-relational data storage technologies (e.g., data warehouses, NoSQL, key-value stores, document stores).
Deep experience with cloud platforms – Azure strongly preferred; AWS or GCP also considered.
Extensive experience designing, building, and optimizing scalable batch and streaming data pipelines using Databricks (Apache Spark, Delta Lake) to support Medallion Architecture and other modern data patterns.
Demonstrated experience with modern cloud data platforms, distributed processing, and production-grade data pipelines, including: Data quality frameworks and observability.
Orchestration tools and job scheduling.
Performance optimization and cost management.
Proven ability to work independently and lead through influence, managing broad, ambiguous technical challenges and delivering high-impact solutions with minimal guidance.
Experience driving cross-functional collaboration across engineering, analytics, product, and business teams to deliver data solutions that enable measurable business outcomes, including AI and advanced analytics.
Solid understanding of statistics, machine learning, experimentation, and data mining concepts used to drive informed decisions.
Demonstrated ability to: Prepare and explore data at scale.
Support model development workflows.
Help validate analytical outputs and production model behavior in partnership with data scientists.
Ability to translate complex analytical and AI needs into scalable, maintainable data solutions and help move data science work from exploration into repeatable, governed, and automated delivery patterns.
Strong communication and storytelling skills to connect technical work with business value and to convert complex findings and trade-offs into clear, actionable recommendations for diverse audiences.

Nice To Haves

Advanced degree (Master’s or PhD) in Computer Science, Data Engineering, Data Science, Statistics, or related field.
Experience operating as a principal or staff-level engineer (or equivalent) in large-scale, cloud-based data environments.
Experience leading or technically directing a small group of engineers or data scientists on complex initiatives, even without formal people-management responsibility.
Background in manufacturing, automotive, industrial IoT, or similar domains where operational and analytical data converge.
Hands-on experience with: MLOps practices and tools for model deployment, monitoring, and lifecycle management.
Data governance, privacy, and security controls in regulated or enterprise environments.
Event-driven and streaming architectures (e.g., Kafka, Event Hubs) integrated with data platforms.
Track record of establishing or contributing to internal standards, frameworks, or reference implementations that are adopted across teams.
Experience presenting architecture, strategy, and technical recommendations to senior leadership and influencing decisions across organizational boundaries.

Responsibilities

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, reliability, and speed of delivery.
Solve complex, ambiguous, and non-standard data engineering problems using advanced analytical and problem-solving techniques, demonstrating strong ownership, risk-aware decision making, and sound technical judgment.
Partner closely with data scientists, analysts, software engineers, product stakeholders, and business leaders to ensure data is accessible, trustworthy, and aligned to high-impact business outcomes and AI initiatives.
Lead AI and data science enablement by defining and delivering high-quality, feature-ready data, experimentation workflows, and scalable patterns for model development, deployment, monitoring, and continuous improvement.
Provide technical leadership for large, multi-sprint or multi-team initiatives, including defining scope, breaking 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 technical concepts, trade-offs, and recommendations clearly to both technical and non-technical audiences, enabling informed decisions at multiple levels of the organization.
Establish repeatable patterns for exposing governed, curated, contract-backed data products and semantic models to analytics and AI applications.
Contribute to multi-agent systems, agent orchestration, supervisor-agent patterns, and reusable AI services that turn governed data and domain context into actionable intelligence.