Senior Site Reliability Engineer, Wikimedia Enterprise

The Wikimedia Foundation is looking for a Senior Site Reliability Engineer to join our team, reporting to the Sr. Engineering Manager. As the Site Reliability Engineer, you will play a key role in designing, developing, and maintaining reliable, scalable, and highly available infrastructure for our API services.

Responsibilities:

• Define, track, and improve Service Level Objectives (SLOs), SLIs, and error budgets to ensure reliability targets are met
• Build and enhance observability systems (metrics, logs, and distributed tracing) to enable proactive detection and faster troubleshooting
• Drive reliability engineering practices, including capacity planning, load testing, and resilience validation (e.g., chaos testing)
• Improve developer experience (DevEx) by enabling self-service infrastructure and streamlining deployment workflows
• Partner with engineering team members to embed reliability best practices early in the development lifecycle
• Design, implement, and optimize CI/CD and GitOps workflows using tools such as GitLab (or similar) and ArgoCD(or similar), enabling automated, reliable deployments with support for progressive delivery strategies like canary and blue-green releases
• Implement secure-by-default infrastructure and enforce best practices (e.g., IAM, secrets management, encryption)
• Continuously optimize infrastructure cost and efficiency using FinOps principles while maintaining performance and availability
• Establish and track operational metrics such as MTTR, MTTD, and incident frequency to drive continuous improvement
• Reduce operational toil by identifying repetitive work and implementing automation-first solutions
• Contribute to and evolve internal platform capabilities that standardize infrastructure and improve scalability across teams
• Collaborating with a global and asynchronously communicating team (don’t worry if you have never worked remotely, we’ll help you get used to it)
• Mentoring peers in your areas of technical and operational strength

Requirements:

• Define, track, and improve Service Level Objectives (SLOs), SLIs, and error budgets to ensure reliability targets are met
• Build and enhance observability systems (metrics, logs, and distributed tracing) to enable proactive detection and faster troubleshooting
• Drive reliability engineering practices, including capacity planning, load testing, and resilience validation (e.g., chaos testing)
• Improve developer experience (DevEx) by enabling self-service infrastructure and streamlining deployment workflows
• Partner with engineering team members to embed reliability best practices early in the development lifecycle
• Design, implement, and optimize CI/CD and GitOps workflows using tools such as GitLab (or similar) and ArgoCD(or similar), enabling automated, reliable deployments with support for progressive delivery strategies like canary and blue-green releases
• Implement secure-by-default infrastructure and enforce best practices (e.g., IAM, secrets management, encryption)
• Continuously optimize infrastructure cost and efficiency using FinOps principles while maintaining performance and availability
• Establish and track operational metrics such as MTTR, MTTD, and incident frequency to drive continuous improvement
• Reduce operational toil by identifying repetitive work and implementing automation-first solutions
• Contribute to and evolve internal platform capabilities that standardize infrastructure and improve scalability across teams
• Collaborating with a global and asynchronously communicating team (don't worry if you have never worked remotely, we'll help you get used to it)
• Mentoring peers in your areas of technical and operational strength
• Automation & Configuration Management: Experience with Infrastructure as Code and automation tools (e.g., Terraform, Ansible) and proficiency in at least one programming language (e.g., Python, Go, or similar)
• Cloud Infrastructure: Experience designing, operating, and optimizing cloud-based systems across platforms such as AWS, Azure, or GCP, including scalability, reliability, and cost efficiency
• CI/CD & Deployment Practices: Experience building and maintaining CI/CD pipelines and GitOps workflows (e.g., GitLab or similar, ArgoCD), with familiarity in progressive delivery approaches such as canary and blue-green deployments
• Incident Management & Reliability Operations: Experience with incident response, on-call practices, and leading postmortems, with a focus on continuous improvement and operational excellence
• SRE Principles & Observability: Strong understanding of SRE best practices, including SLOs, SLIs, and error budgets, along with experience in observability (metrics, logging, and distributed tracing e.g., Prometheus, OpenTelemetry)
• Collaboration & Communication: Ability to work effectively in a distributed, cross-functional environment, with strong documentation and communication skills
• Proven experience operating highly available, large-scale distributed systems, with a deep understanding of reliability, scalability, and failure modes
• Ownership mindset: Takes end-to-end responsibility for system reliability, proactively identifying and addressing risks before they impact users
• Bias for automation: Continuously seeks to reduce operational toil through automation and scalable solutions
• Continuous improvement mindset: Actively learns from incidents and drives improvements through blameless postmortems and iterative enhancements
• Customer and reliability focus: Prioritizes user experience by balancing availability, performance, and cost
• Adaptability and learning: Comfortable working in a fast-evolving environment and learning new tools and technologies as needed
• Familiarity with Wikimedia or other open source projects is a plus
• Experience managing and troubleshooting event streaming platforms at scale (e.g., Kafka, Kinesis, or similar)
• Hands-on experience with cloud platforms such as AWS and/or GCP, including designing and operating production systems
• Familiarity with data lake architectures and large-scale data processing frameworks (e.g., Iceberg, Flink, Spark)
• Experience with continuous profiling and performance optimization tools to identify bottlenecks and improve system efficiency
• Experience working with or contributing to open source projects, particularly in infrastructure or data ecosystems
• Prior participation in the Wikimedia movement