Senior Staff Service Reliability and Operational Intelligence Engineer

Location: Santa Clara, CA
Travel: Up to 25%
Job ID:  1795

The Role: 

The Platform Engineering team builds, secures, and operates scalable infrastructure for cloud-managed SaaS products with on-premises components deployed at customer sites.

The Service Reliability and Operational Intelligence discipline ensures the platform remains stable and resilient, with focus on service continuity and seamless customer experience. It owns production reliability and resilience, observability architecture, service-level objectives, incident response, and implementation of AIOps workflows for triage, remediation, and self-healing. 

As a Senior Staff Service Reliability and Operational Intelligence Engineer, you define the technical direction for reliability across regions and services. You own the reliability strategy, establish the standards and mechanisms that guide production operations, and elevate excellence through design leadership, operational discipline, and mentorship. You stay deeply hands-on by designing and operating observability platforms, defining and governing SLO programs, leading high-severity incident response, and building resilience and disaster-recovery automation. 

The work is driven by observability and automation, with a focus on detecting and fixing issues before customers are affected and using every incident to improve the system.

Responsibilities:

• Own the technical strategy and multi-year roadmap for operational excellence and production readiness across development, pre-production, and production environments. 
• Define and govern the New Service Introduction framework, including mandatory architecture, security, resilience, capacity, observability, supportability, and release-readiness reviews before services enter production. 
• Establish organization-wide service ownership standards covering service catalog records, accountable owners, dependency maps, runbooks, support models, escalation paths, recovery objectives, and on-call readiness. 
• Lead the architecture and evolution of the shared observability platform, establishing consistent standards for logs, metrics, distributed traces, and profiles across production systems. 
• Define standards for dashboards, alert policies, synthetic monitoring, telemetry quality, retention, sampling, cardinality, and cost controls. 
• Own the reliability governance model for production services, including SLIs, SLOs, error budgets, and escalation mechanisms. 
• Connect service-health signals to customer and business impact, enabling early anomaly detection, service-degradation prevention, and rapid isolation of end-user-impacting events. 
• Advance incident-management maturity through consistent severity classification, incident command, stakeholder and executive communications, automated evidence collection, and coordinated response to high-severity incidents. 
• Establish blameless post-incident review practices, ensure remediation actions are tracked to completion, and drive systemic fixes for recurring failure modes. 
• Lead operational capacity and efficiency management, including demand forecasting, cloud and Kubernetes capacity, performance testing, scaling thresholds, headroom policies, resource rightsizing, and capacity-risk reviews. 
• Design and govern AI Ops capabilities for event correlation, alert-noise reduction, predictive detection, probable root-cause analysis, autonomous triage, assisted remediation, and controlled self-healing. 
• Deliver secure AI-agent workflows across observability platforms, service catalog, Jira, Confluence, source control, and CI/CD. 
• Improve on-call effectiveness through sustainable rotation design, operational-readiness standards, escalation policies, diagnostic automation, alert-quality management, and reliable follow-the-sun handoffs. 
• Provide hands-on technical leadership during major incidents, complex reliability investigations, architectural reviews, resilience exercises, and critical service launches. 
• Use operational data, incident trends, service-level performance, capacity signals, change outcomes, and automation effectiveness to prioritize continuous improvement.

Requirements:

• 12+ years of production engineering, site reliability engineering, platform engineering, or cloud operations experience, including recent hands-on reliability work. 
• Recent experience designing and operating large-scale, fault-tolerant production systems on AWS or GCP. 
• Deep understanding of distributed systems, cloud infrastructure, Kubernetes, networking, CI/CD, and production failure modes. 
• Demonstrated ownership of observability architecture, including instrumentation of production systems and governance of metrics, logs, traces, SLIs, SLOs, and error budgets. 
• Proven experience establishing reliability and operational-readiness standards for business-critical services. 
• Hands-on experience designing and executing failure experiments, disaster-recovery exercises, and validated service failovers. 
• Experience personally commanding SEV1 or SEV2 incidents, coordinating technical and executive communications, and driving root causes through to systemic remediation.
• Demonstrated ownership of measurable reliability outcomes such as availability, latency, MTTR, change-failure rate, alert quality, and error-budget adherence. 
• Experience with capacity forecasting, performance testing, scaling strategies, and cloud and Kubernetes resource management. 
• Strong software engineering and automation skills using languages such as Python or Go, infrastructure as code, and modern delivery toolchains. 
• Evidence of multi-team technical leadership through architecture reviews, standards, coaching, and mechanisms adopted beyond a single service or team. 
• Ability to influence cross-functional stakeholders and deliver complex initiatives without relying on direct management authority.

Preferred Qualifications:

• Experience prioritizing operational risk using identity, workload, dependency, and exposure-path context to focus remediation on issues with material customer or business impact. 
• Experience designing AI Ops capabilities for anomaly detection, event correlation, predictive alerting, root-cause analysis, and operational noise reduction. 
• Hands-on experience with autonomous remediation and self-healing workflows using Amazon Bedrock AgentCore or comparable agentic automation frameworks. 
• Experience integrating governed AI agents with operational platforms such as Jira, Confluence, source control, CI/CD, service catalogs, and observability systems. 
• Practical experience with capacity optimization, resource rightsizing, efficiency engineering, telemetry cost management, and FinOps principles. 
• Experience designing and operating load-balancing solutions, health-based failover, global traffic management, and performance optimization for highly available services. 
• Ability to integrate networking, security, resilience, performance, and operability requirements into cohesive platform architecture decisions. 
• Experience with progressive-delivery techniques such as canary deployments, blue-green deployments, automated rollback, and feature-flag governance. 
• Experience establishing sustainable global on-call models and follow-the-sun operational practices. 

The total compensation package includes base, bonus, equity, and a range of benefit options found on our career site.