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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 Qualifications)
- Master’s degree in Computer Science, Data Engineering, Electrical/Mechanical/Industrial Engineering, or related technical field; or equivalent industry experience.
This job may be eligible for relocation benefits.
Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.
Compensation:
- The expected base compensation for this role is: $248,300 \- $315,000\. Actual base compensation within the identified range will vary based on factors relevant to the position.
- Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
- Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation \& holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
GM DOES NOT PROVIDE IMMIGRATION\-RELATED SPONSORSHIP FOR THIS ROLE. DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP NOW OR IN THE FUTURE. THIS INCLUDES DIRECT COMPANY SPONSORSHIP, ENTRY OF GM AS THE IMMIGRATION EMPLOYER OF RECORD ON A GOVERNMENT FORM, AND ANY WORK AUTHORIZATION REQUIRING A WRITTEN SUBMISSION OR OTHER IMMIGRATION SUPPORT FROM THE COMPANY (e.g., H\-1B, OPT, STEM OPT, CPT, TN, J\-1, etc.)
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About GM
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
Why Join Us
We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Benefits Overview
From day one, we're looking out for your well\-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources .
Non\-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
All employment decisions are made on a non\-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role\-related assessment(s) and/or a pre\-employment screening prior to beginning employment. To learn more, visit How we Hire .
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1\-800\-865\-7580\. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Salary Context
This $248K-$315K range is above the 75th percentile for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At General Motors (GM), this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($281K) sits 52% above the category median. Disclosed range: $248K to $315K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Motors (GM) AI Hiring
General Motors (GM) has 13 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Warren, MI, US, Austin, TX, US, Sunnyvale, CA, US. Compensation range: $173K - $335K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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