Senior Site Reliability Engineer, Wikimedia Enterprise Job at Wikimedia Foundation

Job Description

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. You will contribute heavily to the high impact challenges behind innovating, building, and maintaining Wikipedia’s data feeds for high volume reusers. In this role, you will foster cross department collaboration with the wikimedia foundation SRE teams. You will own reliability targets (SLOs) for critical APIs, balancing performance, cost, and availability through data-driven decisions. You will be involved in designing and running the infrastructure and services that interact with the base of Wikimedia Foundation’s projects, including, but not limited to: Kubernetes clusters, application servers, code collaboration infrastructure, and other developer-facing services. You will participate in incident response and be on-call. This role requires frequent work with other members of the enterprise and Foundation SRE team to maintain and improve our systems, as well as interacting with people not in SRE, like Security, Release and Software Engineers, together striving to move our projects and technologies forward.

Job Responsibility

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

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
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

Nice to have

Familiarity with Wikimedia or other open source projects
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

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