AI Data Engineering Manager — Hybrid Leader

General Motors Company; Warren (MI); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Company vehicle program

Total Rewards resources

Job summary

General Motors is seeking an AI Data Engineering Manager to lead a multidisciplinary team delivering industrialized data assets and AI data products. You will translate strategy into a clear roadmap, own data platforms, and ensure scalable, secure, observable data products across batch, streaming, and real-time workloads.

You will guide architecture, data contracts, and MLOps patterns while managing resources, budget, and cross-team collaboration with data scientists, ML engineers, product

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Data Science, or related field or equivalent experience.
• 10+ years delivering scalable data solutions in data/AI.
• 3+ years leading technical teams with coaching and delivery responsibility.
• Advanced knowledge of data and AI platform architecture and data pipelines.

Responsibilities

• Lead diverse teams of data engineers, AI/ML platform engineers, software engineers and tech leads.
• Translate GPSC and Data org strategies into an actionable AI data engineering roadmap and plan.
• Own architecture, delivery, and health of scalable data and AI engineering capabilities.
• Curate and transform structured, semi-structured, unstructured and multimodal datasets for model training and inference.
• Set engineering standards for data contracts, lineage, quality, observability, security, and privacy.

Skills

Team leadership

Stakeholder influence

Strategic roadmap

Communication

MLOps/LLMOps

Education

Bachelor's degree in CS/Software/Data/related

Master's degree (preferred)

Tools

Python

SQL

Apache Spark

Kubernetes

LangChain/CrewAI or similar

Job description

General Motors is seeking an AI Data Engineering Manager to lead a multidisciplinary team delivering industrialized data assets and AI data products. You will translate strategy into a clear roadmap, own data platforms, and ensure scalable, secure, observable data products across batch, streaming, and real-time workloads.

You will guide architecture, data contracts, and MLOps patterns while managing resources, budget, and cross-team collaboration with data scientists, ML engineers, product