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General Motors
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General Motors is a global automotive and manufacturing company working across vehicle engineering, production, transportation, and information technology. Founded in 1908, GM develops technologies aimed at advancing mobility while pursuing its vision of zero crashes, zero emissions, and zero congestion. Its international operations bring together large, multidisciplinary teams focused on innovation, sustainable transportation, and the future of the automotive industry.
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Data Engineering, Cloud Migration and Platforms Engineer - General Motors (Hybrid)

Modernize General Motors’ Oracle workloads through cloud migration and platform engineering. Build secure, governed data platforms, pipelines, infrastructure automation, and reliable operating practices.

CACanada
$97,800 – $146,600 / year
HybridFull-timeMiddleSeniorEnglish RequiredCloud Engineer
ApacheAzureBigQueryCloudETLGoogle Cloud PlatformKafkaKubernetesOraclePulsarPySparkPythonSQLTerraformUnityVault

Description

  • Evaluate Oracle-based architectures, workloads, dependencies, interfaces, data flows, and operating processes to plan and execute migrations.
  • Create scalable batch and near-real-time pipelines for data ingestion, transformation, validation, reconciliation, and publishing.
  • Develop migration approaches for data, schemas, ETL/ELT workloads, stored procedures, interfaces, and downstream consumers.
  • Build and operate cloud data-platform capabilities across Azure, Google Cloud Platform, and multi-cloud environments.
  • Apply Databricks Workflows, Unity Catalog, Delta Lake, MLflow, and Asset Bundles when appropriate.
  • Create reusable Terraform infrastructure as code for cloud, networking, data-platform, and Databricks resources.
  • Design CI/CD workflows and automate deployments and environment promotion across development, test, staging, and production.
  • Apply data-security controls covering identity and access management, encryption, secrets, key rotation, and least privilege.
  • Establish governance practices for cataloging, lineage, classification, retention, auditability, and responsible data use.
  • Implement monitoring, logging, alerting, data-quality checks, and operational dashboards.
  • Increase platform reliability, scalability, performance, and cost efficiency through automation and ongoing optimization.
  • Lead or assist with incident response, service restoration, root cause analysis, and post-incident reviews.
  • Define and support SLAs, SLOs, data-freshness targets, recovery objectives, and error-budget practices.
  • Build automated unit, integration, data-quality, regression, and end-to-end testing.
  • Work with architects, application teams, analytics and AI practitioners, security partners, product owners, and global technology teams.
  • Contribute to two-week sprints, backlog refinement, estimation, delivery planning, demonstrations, and continuous improvement.
  • Maintain architecture diagrams, data-flow records, API and interface documentation, runbooks, onboarding guides, README files, and production-readiness materials.

Requirements

  • Bachelor’s degree in computer science, engineering, information systems, data engineering, or a related field, or equivalent practical experience.
  • At least three years of professional experience in data engineering, cloud platform engineering, DevOps, software engineering, or a related discipline.
  • Proven experience delivering production-grade data solutions.
  • Practical experience designing and supporting data pipelines and ETL/ELT workloads, ideally with Oracle or another enterprise relational database.
  • Strong Python skills for automation, developer tooling, data engineering, and PySpark processing.
  • Experience with SQL, relational modeling, schema design, query optimization, and data reconciliation.
  • Experience with at least one major cloud platform, preferably Azure or Google Cloud Platform.
  • Experience building infrastructure as code with Terraform, including reusable modules, remote state, and environment-specific configuration.
  • Experience using Git, branching strategies, pull requests, code reviews, and automated CI/CD workflows.
  • Experience with a cloud data platform or lakehouse such as Databricks, Delta Lake, BigQuery, Synapse, or an equivalent technology.
  • Knowledge of cloud networking, identity and access management, encryption, secrets management, and secure service-to-service integration.
  • Experience implementing monitoring, logging, alerting, operational dashboards, and data-quality controls.
  • Strong troubleshooting, analytical, communication, and cross-functional collaboration abilities.
  • Preferred: experience migrating Oracle databases, ETL jobs, stored procedures, or data warehouses to cloud services.
  • Preferred: hands-on experience with Databricks Workflows, Unity Catalog, Delta Lake, MLflow, and Asset Bundles.
  • Preferred: familiarity with Azure services such as ADLS Gen2, Key Vault, Entra ID, AKS, Azure networking, and Azure DevOps or GitHub Actions.
  • Preferred: familiarity with Google Cloud services such as Cloud Storage, BigQuery, Dataproc, GKE, Pub/Sub, Secret Manager, Cloud IAM, and VPC networking.
  • Preferred: experience with streaming and event-driven processing using Apache Kafka, Apache Pulsar, Pub/Sub, or equivalent technologies.
  • Preferred: experience with Kubernetes, Azure Container Apps, GKE, or other containerized runtimes.
  • Preferred: experience with Datadog or another observability platform.
  • Preferred: experience with MLflow, model or feature pipelines, experiment tracking, and production monitoring for AI/ML workloads.
  • Preferred: experience with HashiCorp Vault or comparable enterprise secrets-management tools.
  • Preferred: experience in regulated, security-sensitive, or enterprise-scale environments.
  • Preferred: experience with production-readiness reviews, incident management, postmortems, and service-reliability practices.
  • Must not require GM immigration-related sponsorship now or in the future.

Benefits

  • Hybrid work arrangement.
  • Work involving cloud data platforms, analytics, artificial intelligence, and enterprise modernization.
  • Inclusive workplace focused on belonging and professional development.
  • Information provided about role-related assessments and/or pre-employment screening.
  • Accommodation support is available for applicants with disabilities.

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