Job Description

Accountabilities:

• Design, build, and continuously evolve scalable cloud infrastructure using Google Cloud Platform (GCP).
• Develop internal tooling, automation, and self-service capabilities that improve engineering team autonomy and operational efficiency.
• Advance observability across metrics, logging, tracing, and distributed systems to improve system visibility and reduce mean time to recovery.
• Establish infrastructure cost visibility and lead governance and optimization initiatives to improve cloud efficiency.
• Champion reliability engineering practices, including Service Level Objectives (SLOs), Service Level Indicators (SLIs), error budgets, and DORA metrics.
• Define and improve incident management processes, lead incident response, and facilitate post-incident reviews focused on identifying and addressing root causes.
• Participate in an on-call rotation and help ensure reliable operation of production systems.
• Partner with engineering and product teams to identify reliability risks, improve operational practices, and support the delivery of resilient software.
• Contribute to continuous improvements across infrastructure, deployment, monitoring, and operational workflows.

Requirements:

• 3+ years of professional experience in Site Reliability Engineering or production-focused SRE roles.
• Strong hands-on proficiency with Google Cloud Platform (GCP), including cloud cost optimization and governance.
• Practical experience managing Kubernetes clusters and workloads in production environments.
• Infrastructure as Code experience using Terraform, Deployment Manager, or comparable tools.
• Strong scripting and automation capabilities using Python, Bash, Go, or similar languages.
• Experience building and maintaining observability solutions using tools such as Prometheus, Grafana, and OpenTelemetry, along with logging and distributed tracing.
• Proven experience with incident management, post-incident reviews, production troubleshooting, and on-call responsibilities.
• Ability to define, implement, and operationalize SLOs, SLIs, and error budgets.
• Understanding of DORA metrics and their application to software engineering and delivery workflows is an advantage.
• Experience designing and evolving cloud infrastructure at scale is highly desirable.
• Background working in AI/ML, geospatial technology, or similarly data-intensive environments is a plus.
• Strong problem-solving, communication, collaboration, and ownership skills.
• Ability to work effectively in a fully distributed and cross-functional engineering environment.
• Candidates must be based in Canada or another eligible location within North America, the EU, or the UK.
• Visa sponsorship is not available for this role.

Benefits:

• Fully remote work with flexibility across eligible locations.
• Opportunity to work on challenging cloud infrastructure, reliability, and observability initiatives.
• High degree of ownership and influence over infrastructure and operational engineering practices.
• Opportunity to build automation and self-service tooling that directly improves engineering productivity.
• Exposure to modern cloud-native technologies, including GCP, Kubernetes, Terraform, Prometheus, Grafana, and OpenTelemetry.
• Opportunity to contribute to reliability engineering practices involving SLOs, SLIs, error budgets, incident management, and DORA metrics.
• Salary discussed during the interview process and determined based on experience and location.
• Opportunity to collaborate with experienced engineering teams in an innovative AI/ML and technology-driven environment.