Staff Data Engineer — AI & Cloud

GM•Austin, TX

5d•Hybrid

About The Position

This role is categorized as hybrid. This means the successful candidate is expected to report to Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. A Staff Data Engineer operates as a recognized expert in data engineering with strong cross-functional visibility, leading complex initiatives that improve GM’s data platforms, cloud architecture, and AI-enablement capabilities. This role designs and delivers scalable, secure, cloud-native data solutions; builds highly automated, performant pipelines; and enables advanced analytics and machine learning use cases across the enterprise.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Science, Business Analytics, Engineering, or a related field; or equivalent experience.
8–10+ years in data engineering or related technical leadership roles
Strong expertise in Python, SQL, Scala, or R with a focus on large-scale data processing, optimization, and performance tuning.
Extensive experience with big data frameworks such as Azure Databricks, Apache Spark, Kafka, or Hadoop for large-scale data processing.
Deep knowledge of cloud platforms and data services such as Azure, AWS, or GCP.
Strong command of data modeling, relational and NoSQL databases, and scalable storage design.
Strong understanding of data governance, privacy, and security controls for enterprise data systems.
Demonstrated ability to lead large-scale technical initiatives and align engineering outcomes to broader business goals.
Strategic thinking with the ability to align technical solutions to GM business priorities.
Strong communication and stakeholder management across technical and non-technical audiences.
Advanced problem solving, architectural judgment, and continuous learning mindset.

Nice To Haves

Master’s degree in Computer Science, Data Engineering, or a related field
Experience enabling or building AI/GenAI data foundations, including embedding pipelines, RAG/vector search, or production AI workflows.
Experience with Docker, Kubernetes, and cloud-native deployment/operations patterns.
Familiarity with observability and production reliability for data and AI systems.
Proven ability to influence enterprise standards, mentor senior engineers, and drive adoption of new capabilities across teams.
Experience in Automative or Manufacturing Industry is preferred

Responsibilities

Design and evolve scalable, high-performance data architectures, including data warehouses, data lakes, and mesh environments
Design ML models and enable AI/ML use cases by integrating data pipelines with machine learning platforms, feature/data products, vector or retrieval pipelines, and model-serving workflows where needed.
Lead the design and implementation of large-scale data pipelines, data architectures, and cloud-based data platforms for ingestion, transformation, storage, and consumption.
Establish and enforce organization-wide best practices for data quality, lineage, observability, security, and CI/CD operations
Architect and optimize solutions on cloud platforms such as Azure, AWS, or GCP, using native data services and containerized/orchestrated deployments where appropriate.
Partner with data scientists, analysts, and executive stakeholders to translate complex business requirements into technical roadmaps
Mentor engineers and raise technical capability across the organization in areas such as distributed data processing, cloud engineering, AI-enablement, and platform reliability.
Identify and integrate emerging technologies, such as Generative AI (GenAI), into data workflows for automated validation and performance tuning.