Data Engineering, Cloud Migration & Platforms Engineer

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 existinglegacyenvironment 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-privilege controls in alignment with GM policies and cloud best practices.
• Establish data governance practices including cataloging, lineage, classification, access controls, retention, auditability, and responsible use of enterprise data.
• Design and maintain monitoring, logging, alerting, data quality checks, and operational dashboards that provide clear visibility into pipeline, platform, and service health.
• Improve reliability, scalability, performance, and cost efficiency through automation, resilient design, capacity planning, and continuous optimization.
• Lead or support incident response, service recovery, root cause analysis, and post-incident reviews for data-platform and pipeline issues.
• Define and support operational targets such as SLAs, SLOs, freshness objectives, recovery objectives, and error-budget-aware