Data Platform Devops Engineer

Description:

Data Platform DevOps Engineer - Senior Consultant

Posting Start Date: 6/12/26

Job Type: PermanentWork Model: HybridReference code: 133554Primary Location: Toronto, ONAll Available Locations: Toronto, ON; Montreal, QC

Our Purpose

At Deloitte, our Purpose is to make an impact that matters. We exist to inspire and help our people, organizations, communities, and countries to thrive by building a better future. Our work underpins a prosperous society where people can find meaning and opportunity. It builds consumer and business confidence, empowers organizations to find imaginative ways of deploying capital, enables fair, trusted, and functioning social and economic institutions, and allows our friends, families, and communities to enjoy the quality of life that comes with a sustainable future. And as the largest 100% Canadian-owned and operated professional services firm in our country, we are proud to work alongside our clients to make a positive impact for all Canadians.

By living our Purpose, we will make an impact that matters.

Have many careers in one Firm.
Enjoy flexible, proactive, and practical benefits that foster a culture of well-being and connectedness.
Learn from deep subject matter experts through mentoring and on the job coaching

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As a Data & AI Platform Engineer, you operate at the foundation of modern data and AI ecosystems—designing, deploying, and operating scalable, secure, and reliable platforms that enable enterprise data, analytics, and agentic AI workloads. You focus on platform engineering, automation, and reliability, ensuring that data platforms are production-grade, governed, and optimized for performance and cost.

What will your typical day look like?

 Design and deploy modern data platforms across cloud and hybrid environments, including Snowflake, Databricks, and SAS Viya.
 Engineer platform infrastructure using Infrastructure as Code (IaC) tools such as Terraform, enabling repeatable and scalable deployments.
 Build and manage CI/CD pipelines to automate platform provisioning, testing, releases, and upgrades using tools like Azure DevOps, GitHub Actions, or Jenkins.
 Deploy, manage, and scale Kubernetes-based environments to support distributed data and AI workloads.
 Define and implement Policy as Code frameworks to enforce governance, compliance, and security standards across environments.
 Design and implement secure cloud architectures, including IAM, networking, encryption, secrets management, and data access controls.
 Develop high availability and disaster recovery strategies, including multi-region deployments, failover mechanisms, and backup strategies.
 Optimize platform performance, scalability, and cost efficiency through monitoring, tuning, and right-sizing strategies.
 Implement observability frameworks (logging, monitoring, alerting) using tools such as Prometheus, Grafana, Azure Monitor, or Datadog.
 Operate and maintain production platforms, including upgrades, patching, incident response, and root cause analysis.
 Apply Site Reliability Engineering (SRE) principles, including SLO/SLI definition, error budgets, automation, and reliability improvements.
 Collaborate with data engineers, AI engineers, architects, and business stakeholders to ensure platforms meet enterprise needs.
 Support agentic AI and advanced analytics workloads by enabling scalable, secure, and performant infrastructure foundations.
 Drive platform engineering best practices, including automation, standardization, and reusable deployment patterns.

About The Team

AI & Data is one of Deloitte Canada’s fastest-growing practices, with 300+ professionals across Toronto, Montreal, Vancouver, Ottawa, and Calgary. Our multidisciplinary team brings together engineers, architects, data scientists, and strategists to build modern data platforms and AI solutions that deliver measurable, real-world impact.

Enough About Us, Let’s Talk About You

You will thrive in this role if:

You enjoy building and operating large-scale distributed systems, are passionate about automation and cloud-native engineering, and bring a strong mindset around reliability, security, and governance. You are curious about how platforms enable data and AI innovation—including emerging agentic AI architectures—and are motivated to continuously improve engineering practices.

Qualifications

 Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
 Minimum of 5+ years of experience in platform engineering, cloud engineering, DevOps, or SRE roles.
 Strong experience deploying and operating data platforms such as Snowflake, Databricks, and/or SAS Viya.
 Deep expertise in Infrastructure as Code (Terraform preferred) and automated provisioning.
 Experience with containerization and orchestration technologies (Docker, Kubernetes).
 Hands-on experience with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins) and DevOps practices.
 Strong understanding of cloud platforms (Azure, AWS), including networking, IAM, storage, and security services.
 Experience implementing Policy as Code, governance frameworks, and compliance standards.
 Experience designing for high availability, resilience, disaster recovery, and fault tolerance.
 Strong knowledge of observability practices, including logging, monitoring, and alerting frameworks.
 Experience with performance tuning, scalability optimization, and cost management in cloud environments.
 Strong system engineering and troubleshooting skills across distributed systems.
 Experience working in agile, cross-functional teams delivering production-grade platforms.
 Bilingual proficiency in French and English (written and spoken).
 Relevant certifications (Azure, AWS, Kubernetes, Terraform, Snowflake, Databricks) are considered an asset.