Jobs / United States / General Motors Company
Senior Data Engineer – Agentic AI, Automation, and Data Platforms
General Motors Company · 🇺🇸 2 Locations
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What General Motors Company paid sponsored hires in similar roles2 certified filings for “Senior ML Engineer - Embodied AI Offboard Percepti” (Data Scientists) in CA: $230k–$240k, median $235k. Most were filed at wage level III (100%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — General Motors Company
The US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for General Motors Company →
About the role
Job Description This role is categorized as hybrid. This means the successful candidate is expected to report to GM Warren Global Technical Center or Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. The Role This role is for a senior individual contributor in Data Engineering who can independently lead complex technical work, apply strong professional judgment, improve processes and delivery patterns, and move quickly from ideas to production solutions. At this level, the individual is expected to operate with minimal guidance, resolve non-standard problems using advanced analytical thinking, take ownership of outcomes, and serve as a technical resource for less experienced team members. The role is anchored in data engineering with a strong focus on automation and Agentic AI. The engineer will build reliable data platforms and use technologies such as Cursor, large language models, Vector Search, Databricks agents, RAG, and similar tools to accelerate engineering delivery and enable intelligent data experiences. The engineer will partner with data scientists and ML engineers as needed to support experimentation and productionize AI solutions, while data engineering and platform delivery remain the primary focus. What You’ll Do • Design, build, and productionize reliable, scalable, and secure data pipelines and data products in Azure Databricks that support AI, analytics, and operational use cases. • Transform raw data from multiple source systems into trusted, well-structured data products for analytics, model development, LLM applications, Vector Search, and AI agents. • Build automation and reusable engineering workflows using tools such as Cursor, Claude, LLMs, Databricks agents, and related technologies to improve development speed, testing, documentation, troubleshooting, and operational efficiency. • Design and enable governed data, retrieval, and semantic patterns for Vector Search, RAG, Databricks agents, Genie, Glean, and other AI-enabled applications. • Build and optimize batch and streaming pipelines, including feature-ready, training, inference, and model-scoring data workflows, in partnership with data science teams when needed. • Establish practical engineering patterns for CI/CD, automated testing, data quality, lineage, observability, security, cost management, and production support. • Solve complex data engineering, performance, reliability, and data-quality problems with strong ownership, urgency, and sound technical judgment. • Contribute to technical direction, reusable standards, and delivery practices across teams; influence adoption through working examples and measurable outcomes. • Mentor team members through technical guidance, design reviews, knowledge sharing, and strong engineering practices. Your Skills & Abilities (Required Qualifications) • Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or related field, or equivalent experience. • 5+ years of relevant professional experience • Strong experience in data engineering, including pipeline development, data modeling, data integration, distributed processing, and production support for enterprise data platforms. • Experience using Python or Scala, SQL, Apache Spark, and modern cloud data platforms; Azure is preferred, and AWS or GCP experience is also considered. • Experience designing, building, and optimizing scalable batch and streaming data pipelines using Databricks, Delta Lake, and medallion or comparable lakehouse architecture. • Hands-on experience using AI-assisted development or automation tools such as Cursor, Claude, GitHub Copilot, or comparable platforms to improve engineering productivity and delivery. • Hands-on