Reliability & Incident Response 

• Monitor production systems using observability tooling — dashboards, alerts, and logs — to detect and triage issues before they impact end users 

• Participate in on-call rotations, respond to incidents following established runbooks, and escalate appropriately when needed 

• Contribute to blameless post-mortems, documenting root causes and follow-up action items to prevent recurrence 

• Help maintain and improve SLO dashboards and alerting thresholds to ensure platform health is visible and measurable 

Toil Reduction & Automation 

• Identify repetitive manual tasks and build automation to eliminate them, reducing toil for yourself and the broader team 

• Write and maintain scripts, tooling, and CI/CD pipeline components that improve deployment reliability and operational efficiency 

• Support self-serve infrastructure initiatives that allow engineering teams to safely provision and manage their own resources 

Platform Operations & Cloud Infrastructure 

• Operate and maintain workloads running on GCP — including GKE, Cloud Run, BigQuery, Pub/Sub, GCS, and Composer 

• Apply Infrastructure-as-Code practices (Terraform, Helm) to consistently and safely manage and version infrastructure changes 

• Support multi-cloud awareness across GCP and Azure, following team standards for consistency and security across environments 

• Adhere to data security and governance policies — IAM best practices, secrets management, encryption, and audit logging 

Collaboration & Growth 

• Work closely with Data Engineering, AI Platform, and Software Engineering teams to ensure reliability is considered from design through deployment 

• Participate in reliability reviews, design discussions, and team ceremonies, contributing ideas and raising operational concerns early 

• Engage with AI and agentic platform workloads, gaining exposure to the operational patterns of LLM-based systems and data pipelines 

• Continuously develop your technical skills and SRE craft, supported by team knowledge-sharing, documentation, and hands-on experience 

Required Qualifications 

• Bachelor's degree in Computer Science, Engineering, or related field (or equivalent practical experience) 

• 6+ years of experience in Site Reliability Engineering, DevOps, or Platform/Infrastructure Engineering in production environments 

• Hands-on experience with GCP services — particularly GKE, Cloud Run, BigQuery, Pub/Sub, and GCS 

• Working proficiency with Infrastructure-as-Code tools such as Terraform or Helm 

• Familiarity with observability tooling — metrics, logging, tracing, and alerting (e.g., Cloud Monitoring, Datadog, or Prometheus/Grafana) 

• Understanding of SLO/SLI concepts and how they relate to production reliability and on-call operations 

• Exposure to data security fundamentals: IAM, encryption, secrets management, and network policies 

• Proficiency in at least one scripting or systems language (Python, Bash, or Go) for automation and operational tooling 

• Strong communication skills with the ability to clearly document incidents, runbooks, and technical processes 

Technical Familiarity 

• Experience with container orchestration — Kubernetes or GKE — and the operational patterns around deploying and managing containerized workloads 

• Basic understanding of CI/CD pipelines and GitOps workflows (ArgoCD, GitHub Actions, or similar) 

• Comfort working with data platforms — familiarity with batch or streaming data pipelines is a plus 

• Awareness of multi-cloud concepts, particularly across GCP and Azure 

Desirable Experience 

• Experience working in retail, e-commerce, or consumer goods environments 

• Familiarity with Google's SRE principles — error budgets, toil tracking, and production readiness reviews 

• Exposure to AI or ML platform operations, including monitoring model serving infrastructure 

• Experience with FinOps or cloud cost visibility tooling