Senior Site Reliability Engineer, Environment Automation

GitLab

6h•Remote

About The Position

As a Site Reliability Engineer (SRE) at GitLab, you’ll help keep all user-facing services and production systems reliable, scalable, and efficient. Our SREs combine a pragmatic operations mindset with strong software engineering practices to drive automation, reduce toil, and improve resilience across our platform. In the Environment Automation specialization, your focus is on operating and automating hundreds of GitLab environments—from initial provisioning to day-to-day maintenance tasks. Unlike other SRE roles, this position centers on automating the lifecycle of many tenant environments, ensuring they remain secure, consistent, and reliable at scale. Some examples of the projects you could work on: Designing infrastructure automation that provisions and operates GitLab environments using Terraform, Ansible, and Kubernetes Creating and maintaining deployment packages for GitLab, such as Helm Charts and omnibus-gitlab Building and operating Dedicated GitLab instances integrated with cloud-native services (e.g., GCP, AWS) Developing tools to orchestrate infrastructure-as-code workflows across multiple tenants Deploying and managing microservices on Kubernetes clusters at scale Enhancing GitLab’s observability stack (e.g., Prometheus, ELK) to support proactive monitoring and incident response Integrating with and operating infrastructure in cloud provider ecosystems (e.g., IAM, networking, storage) Championing and implementing cloud security best practices across automated infrastructure

Requirements

Production-Scale Experience: Proven ability to operate and troubleshoot production workloads across multiple tenants or environments. Deep understanding of how distributed systems fail at scale and how to build in resilience.
Terraform & IaC Mastery: Strong hands-on experience with Terraform, including workspace strategies, state management, and automation patterns that scale. Comfortable solving state isolation issues and building reliable, reusable infrastructure code.
Kubernetes in Production: Skilled at diagnosing deployment failures, interpreting pod logs, and debugging scheduling issues and rollback scenarios in live environments. Understands how pods, ReplicaSets, and controllers interact in production.
Programming & Code Analysis: Ability to read and debug code in Go and/or Ruby. Familiar with identifying performance issues, scalability concerns, and contributing to infrastructure tooling through thoughtful code analysis.
Large Scale Operations Background: Experience supporting infrastructure for many customers or environments simultaneously. Comfortable managing isolation, scaling, monitoring, and incident response across diverse workloads.
Architecture & Incident Response: Able to reason through complex systems and operational challenges. Brings on-call experience and can lead technical discussions and incident resolution efforts under pressure.
Customer-Focused Collaboration: Proven ability to work across teams and with internal or external customers to solve technical problems while maintaining service commitments and clear communication.
GitLab Platform Proficiency: Comfortable using GitLab as a daily tool for infrastructure automation, collaboration, and operational workflows.

Nice To Haves

Experience with Ansible and templating tools like Jsonnet is a plus.

Responsibilities

Build & Scale Multi-Tenant Infrastructure: Design and implement automation that provisions and manages hundreds of isolated GitLab environments using Terraform, Ansible, and Kubernetes. Manage complex state strategies and workspace configurations to support scale and maintainability.
Debug & Resolve Production Issues: Troubleshoot issues across Kubernetes clusters, cloud services, and GitLab apps—identifying root causes of failed deployments, crash loops, and scheduling conflicts to ensure service continuity.
Automate Operations at Scale: Replace manual workflows with infrastructure-as-code solutions, including automated version upgrades, configuration rollouts, and provisioning pipelines that operate reliably across all tenants.
Monitor & Predict Capacity: Build observability systems that detect bottlenecks, predict usage trends, and optimize resource consumption using tools like Prometheus, ELK, and Grafana.
Respond & Lead During Incidents: Lead incident response and postmortem efforts, applying technical depth to resolve issues and establish operational standards that reduce future risk.
Architect & Collaborate: Influence architectural decisions around automation, scalability, and operational excellence. Partner with engineering teams to improve automation, platform resilience, and production-readiness.