Data Platform DevOps Engineer - Senior Consultant

Job summary

As a Data & AI Platform Engineer, you will design, deploy, and operate scalable, secure, and reliable platforms for enterprise data, analytics, and agentic AI workloads. You will focus on platform engineering, automation, and reliability, ensuring data platforms are production-grade, governed, and optimized for performance and cost.

Benefits

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

Responsibilities

• 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 for 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
• 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

Skills

Certifications

Degrees

Languages

Relocation