This role pays more than
91%
of similar roles. Most pay
$141,800–$223,893
— the shaded band above.
At the midpoint, this role pays about
$282k
versus about
$183k
for comparable roles.
Based on 240 similar postings.
Employer
About General Motors (GM)
General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.
General Motors (GM) currently has
116 open roles
on FindRole.
Listed pay typically runs
$160,200–$245,000
across 59 roles with salary data.
As an Agentic Data Engineer, you will serve as a technical expert designing and scaling industrial-grade data and AI platforms that power vehicle product engineering from design through manufacturing. You will be responsible for end-to-end technical strategy, building self-healing, agentic data systems to monitor engineering processes, detect anomalies in parts or quality signals, and provide proactive remediations. Your daily work involves developing high-reliability batch and streaming pipelines, creating semantic models, and implementing agentic workflows using LLMs, rules engines, and machine learning models. You will utilize Python, Scala, or Java alongside tools like LangGraph, AutoGen, Semantic Kernel, and LlamaIndex to build production-grade services. This role solves complex problems in vehicle design modernization by integrating large-scale distributed systems with intelligent automation to improve safety, quality, delivery, and cost across global manufacturing environments.
Design and scale industrial-grade data platforms and AI-ready products for vehicle engineering processes.
Develop high-reliability batch and streaming pipelines from product design systems like Team Center.
Build agentic workflows using LLMs and machine learning to monitor process health and detect anomalies.
Implement automated root cause investigations and trigger context-rich alerts or remediations for engineering teams.
Establish AgentOps practices including evaluation, guardrails, tracing, and failure analysis for factory-facing tools.
Define enterprise-level standards for data ingestion, semantic modeling, and reusable agentic workflows.
Mentor senior engineers and lead technical design reviews to improve the team's technical bar.
Translate complex technical concepts into decision frameworks for senior leadership to influence investment and strategy.
What we're looking for
Bachelor's degree in Computer Science, Data Engineering, Electrical/Mechanical/Industrial Engineering, or a related technical field (or equivalent experience).
Master's degree in Computer Science, Data Engineering, Electrical/Mechanical/Industrial Engineering, or a related technical field (preferred).
12+ years of industry experience delivering large-scale solutions and leading technical direction in relevant domains.
8+ years of experience in data engineering or closely related roles.
Mastery in designing and operating large-scale batch and streaming data platforms on cloud infrastructure.
Experience building reliable, observable pipelines using Python, Scala, or Java with modern orchestration tools.
Proficiency in data modeling (relational, columnar, time-series) and performance optimization for analytics and ML.
Strong literacy in data science and machine learning.
Experience working with LangGraph, AutoGen, Semantic Kernel, or LlamaIndex (preferred).
SQL
Python
LLM
Claude
Snowflake
BigQuery
dbt
Airflow
Prompt Engineering
AI Agents
Natural Language Interfaces
Semantic Layers
Data Engineering
Data Science
Automation