Agentic Data Engineer

About this role

Job Description 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].

The Role The Agentic Data Engineer is a pre‑eminent technical expert who designs, builds, and scales industrial-grade data and AI platforms that power vehicle product engineering all the way from Design through manufacturing and customer support. This role requires deep fluency in data science and agentic AI and is responsible for end‑to‑end technical strategy and execution for Vehicle Product engineering data and intelligent automation.

You will combine deep vehicle product engineering/process understanding with modern data engineering, large-scale distributed systems, and AI/ML literacy to create self‑healing, “agentic” data and decision systems that continuously monitor vehicle design and engineering processes, CAD, detect and explain anomalies, and proactively recommend or execute actions that improve safety, quality, delivery, and cost.

What You’ll Do • Serve as the principal engineer for vehicle product engineering data platforms and AI‑ready data products supporting the global vehicle design process modernization. • Define and drive a technical roadmap for vehicle product engineering and agentic automation in engineering, aligned with GM’s Zero Crashes, Zero Emissions, Zero Congestion vision with end‑to‑end ownership, global impact, and cross‑functional influence. • Establish reference architectures, governance, and best practices for: • High‑reliability batch and streaming pipelines from vehicle product design systems like Team Center and others. • Semantic models and curated data products for vehicle product domains (design, develop, manufacture). • Observability, lineage, quality, and cost management across data and compute layers.

Agentic and Intelligent Systems • Design and lead implementation of agentic workflows that use LLMs, rules engines, and ML models to: • Continuously monitor vehicle design and engineering process health and critical KPIs • Detect anomalies and drift in parts, process, or quality signals and automatically investigate root causes. • Trigger context‑rich alerts, recommended actions, and where appropriate, closed‑loop remediations in collaboration with engineering teams.

• Partner with data scientists and direct responsible engineers (DREs) to turn high‑value models into robust, production‑grade services and agents with clear SLAs, feedback loops, and human‑in‑the‑loop controls. • Define and champion AgentOps practices (evaluation, guardrails, tracing, failure analysis, continuous improvement) for factory‑facing agents and copilots used by engineers, operators, and leaders.

End‑to‑End Delivery and Impact • Own design and delivery of the most complex and strategically critical data engineering initiatives in vehicle development—from discovery and technical design through implementation, rollout, and ongoing optimization. • Solve previously unsolved or ambiguous problems using first‑principles thinking and creative, strategically sound technical approaches; set new standards for how data is captured, managed, and used. • Ensure solutions are secure, resilient, and scalable across plants and regions, with strong attention to change management, operability, and support models. • Translate complex technical and analytical concepts into clear narratives and decision frameworks for senior leaders; influence roadmaps, investment decisions, and prioritization across multiple organizations.

Standards, Governance, and Reuse • Define enterprise‑level patterns, standards, and reusable components for: • Ingestion of data, systems and test equipment into cloud platforms. • Data quality rules, monitors, and remediation playbooks. • Semantic modeling, metric definitions, and KPI libraries for vehicle product engineering. • Agentic workflows (templates, toolkits, APIs) that can be applied across plants and use cases.

• Shape and guide technology selection (e.g., lakehouse, streaming, orchestration, feature stores, agent frameworks) and drive convergence on common platforms while balancing local constraints.

Leadership, Mentorship, and Influence • Operate as a recognized pre‑eminent expert in data engineering and agentic systems—sought out across GM as the authority for strategy, design reviews, and complex incident/problem resolution. • Mentor and coach senior and staff‑level data engineers, data scientists, and ML engineers; elevate technical bar through design reviews, architecture forums, and pair design/implementation. • Act as a multiplier by building communities of practice (data engineering, AgentOps) and enabling others through documentation, patterns, and internal training. • Represent GM externally (conferences, standards bodies, strategic partners) as an industry‑level expert in data platforms and intelligent automation.

Your Skills & Abilities (Required Qualifications) • Bachelor’s in Computer Science, Data Engineering, Electrical/Mechanical/Industrial Engineering, or related technical field; or equivalent industry experience. • 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 • Strong focus on large‑scale, mission‑critical systems • Demonstrated mastery in: • Designing and operating large‑scale data platforms (batch + streaming) on cloud infrastructure. • Building reliable, observable pipelines in languages such as Python/Scala/Java and using modern orchestration and workflow tools. • Data modeling (relational, columnar, time‑series) and performance optimization for analytics, ML, and real‑time decisioning.

• Strong literacy in data science and ML • Experience working with LangGraph, AutoGen, Semantic Kernel, or LlamaIndex

What Can Give You a Competitive Advantage (Preferred Qu

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