Jobs / United States / General Motors Company
Data Engineering, Cloud Migration & Platforms Engineer
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 Vacancy Status: This posting is for a new vacancy within the organization and is open to new applications. AI Disclosure: As part of the application process, Artificial Intelligence will be used in the hiring process for this role This role is categorized as hybrid. This means the successful candidate is expected to report to the Markham Elevation Centre or the Oshawa Elevation Centre at least three times per week. The Team & Opportunity The Enterprise Data team is responsible for supporting and modernizing the data platforms that enable analytics, artificial intelligence, customer engagement, strategic planning, and other enterprise capabilities across GM. We are building a new team to support the existing legacy environment while migrating and transforming the architecture to a cloud-based platform. This role will help establish the engineering standards, reusable platform capabilities, delivery automation, and operational practices required for a secure, scalable, governed, and reliable cloud data ecosystem. The ideal candidate combines strong data engineering fundamentals with cloud platform engineering, automation, and production operations. They are comfortable working across architecture, development, infrastructure, security, quality engineering, and incident response to deliver sustainable data products and services. The Role As a Data Engineering, Cloud Migration & Platforms Engineer, you will design, build, migrate, automate, and support data platforms and pipelines across on-premises and cloud environments. You will contribute to the modernization of Oracle-based enterprise data workloads, develop cloud-native data solutions, and help create the engineering foundation for governed analytics and AI. This role combines data engineering and platform engineering responsibilities, including data pipeline development, cloud infrastructure as code, CI/CD, environment promotion, security and access controls, observability, reliability engineering, testing, documentation, and operational support. The specific cloud implementation may be Azure, Google Cloud Platform, or a multi-cloud architecture, depending on platform direction and business requirements. Key Responsibilities • Assess existing Oracle-based data architecture, workloads, dependencies, interfaces, data flows, and operational processes to support migration planning and execution. • Design and implement scalable data pipelines for batch and near-real-time processing, including ingestion, transformation, validation, reconciliation, and publishing. • Develop migration patterns for data, schemas, ETL/ELT workloads, stored procedures, interfaces, and downstream consumers while maintaining data quality and business continuity. • Build and maintain cloud data-platform capabilities using services such as Azure Data Lake Storage Gen2, Azure Databricks, Azure Kubernetes Service, or comparable Google Cloud services such as Cloud Storage, Dataproc, BigQuery, GKE, and Pub/Sub. • Use Databricks capabilities including Workflows, Unity Catalog, Delta Lake, MLflow, and Asset Bundles where appropriate to support governed data engineering and AI/ML workloads. • Develop reusable infrastructure as code using Terraform, including cloud, networking, data-platform, and Databricks resources; manage remote state and reusable modules. • Design and support CI/CD workflows for data pipelines, infrastructure, configuration, and platform components using GitHub Actions, Azure Pipelines, or comparable cloud-native tooling. • Automate deployment and environment promotion across development, test, staging, and production environments. • Implement data security, identity, access management, encryption, secrets management, key rotation, and least-privil