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Added 8 mins ago
Site Reliability Engineer
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Full-time
As an Intermediate Site Reliability Engineer, you will support and continuously improve enterprise Azure and Databricks platforms.
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General Motors

Data Engineering, Cloud Migration & Platforms Engineer

Markham, ON, Canada

Pay: The salary range for this role is $97,800 to $146,600.
Job type: Full-time
Apply by: Oct 7, 2026
Posting language: English
Posted: Sep 30, 2026

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Original job description

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-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 practices.

Develop automated unit, integration, data-quality, regression, and end-to-end tests for pipelines and platform components.

Collaborate with data architects, application teams, analytics and AI practitioners, security partners, product owners, and global technology teams to translate requirements into reliable data solutions.

Participate in two-week sprints and contribute to backlog refinement, estimation, delivery planning, demos, and continuous improvement.

Create and maintain architecture diagrams, data-flow documentation, API and interface documentation, runbooks, onboarding guides, README files, and production-readiness materials using tools such as Confluence, Lucidchart, and Jira.

Required Qualifications and Skills

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Engineering, or a related discipline, or equivalent practical experience.

At least 3 years of professional experience in data engineering, cloud platform engineering, DevOps, software engineering, or a related field, with demonstrated experience delivering production data solutions.

Hands-on experience designing and supporting data pipelines and ETL/ELT workloads, preferably involving Oracle or another enterprise relational database platform.

Strong Python experience for automation, developer tooling, data engineering, and PySpark-based processing.

Experience with SQL, relational data modeling, schema design, query optimization, and data reconciliation.

Experience with at least one major cloud platform, preferably Azure or Google Cloud Platform, and the ability to learn or support the other.

Experience with infrastructure as code using Terraform, including reusable modules, remote state, and environment-specific configuration.

Experience with Git, branching strategies, pull requests, code reviews, and automated CI/CD practices.

Experience with a cloud data platform or lakehouse technology such as Databricks, Delta Lake, BigQuery, Synapse, or an equivalent platform.

Understanding 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 skills.

Preferred Qualifications

Experience migrating Oracle databases, ETL jobs, stored procedures, or data warehouses to Azure, Google Cloud Platform, Databricks, BigQuery, or equivalent cloud services.

Strong hands-on experience with Databricks Workflows, Unity Catalog, Delta Lake, MLflow, and Asset Bundles.

Azure experience with ADLS Gen2, Key Vault, Entra ID, AKS, Azure networking, and Azure DevOps or GitHub Actions.

Google Cloud experience with Cloud Storage, BigQuery, Dataproc, GKE, Pub/Sub, Secret Manager, Cloud IAM, and VPC networking.

Experience with streaming and event-driven data processing using Apache Kafka, Apache Pulsar, Pub/Sub, or an equivalent technology.

Experience with Kubernetes, Azure Container Apps, GKE, or other containerized runtime environments.

Experience with Datadog or another observability platform.

Familiarity with MLflow, model or feature pipelines, experiment tracking, and production monitoring for AI/ML workloads.

Experience with HashiCorp Vault or comparable enterprise secrets-management tools.

Experience working in regulated, security-sensitive, or enterprise-scale environments.

Experience with production readiness reviews, incident management, postmortems, and service reliability practices.

Compensation:

The salary range for this role is $97,800 to $146,600. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.

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.



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 role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.

This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.



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.

Non-Discrimination and Equal Employment Opportunities

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.

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.

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More about this job

Responsibilities

You will design, build, and migrate data platforms and pipelines across on-premises and cloud environments while establishing engineering standards. The role involves automating deployments, ensuring data security, and maintaining operational health for enterprise data workloads.

Requirements

Candidates must have a bachelor's degree in a relevant field and at least 3 years of professional experience in data or cloud engineering. Proficiency in Python, SQL, cloud platforms, and infrastructure-as-code tools like Terraform is required.

Skills

Data Engineering
Cloud Migration
Python
SQL
Terraform
Databricks
Azure
Google Cloud Platform
CI/CD
Infrastructure as Code
ETL/ELT
Data Governance
Kubernetes
Observability
Data Modeling
PySpark

Visa sponsorship

Not detected in the job text

Keywords

Data Engineering
Cloud Migration
Oracle
Azure Data Lake Storage
Azure Databricks
Azure Kubernetes Service
Google Cloud Platform
BigQuery
Dataproc
Unity Catalog
Delta Lake
MLflow
Terraform
GitHub Actions
CI/CD
Identity and Access Management
Data Governance
Observability
PySpark
Infrastructure as Code
Data Pipelines
Relational Databases
Automation
Reliability Engineering
API
Jira
Confluence
Lucidchart
Enterprise Data
Cloud-native

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