Staff Site Reliability Engineer

• Define, instrument, and enforce SLOs, SLIs, and error budgets across all platform services, ensuring alignment with business and product commitments
• Drive continuous reduction in MTTD and MTTR through improved observability, automated alerting, and runbook-driven incident response
• Lead blameless post-mortems and translate findings into durable reliability improvements, ensuring systemic issues are eliminated rather than patched

Toil Reduction & Automation

• Systematically identify, measure, and eliminate operational toil; track toil percentage per sprint and enforce guardrails to keep it below 50% of engineering capacity
• Build and maintain self-serve infrastructure capabilities - enabling product and data engineering teams to provision, scale, and operate their own resources safely and consistently
• Automate deployment pipelines, configuration management, and operational workflows using Infrastructure-as-Code principles (Terraform, Helm, GitOps)

Platform Engineering & Architecture

• Serve as the primary GCP subject matter expert - architecting and optimizing workloads across GKE, Cloud Run, BigQuery, Pub/Sub, GCS, Composer, Dataflow, and Vertex AI
• Lead multi-cloud architecture decisions across GCP and Azure, ensuring consistent security posture, cost efficiency, and operational practices across environments
• Design and implement self-healing infrastructure patterns, auto-scaling strategies, and capacity planning models to support high-availability data and AI platforms
• Champion data security and governance best practices - including encryption at rest and in transit, IAM least-privilege, secrets management, and audit logging

AI, Agentic Systems & Modern Observability

• Apply SRE principles to agentic AI workloads - defining reliability expectations for LLM-based and multi-agent systems, including latency SLOs, fallback patterns, and model observability
• Partner with AI Platform teams to productionize agentic pipelines with robust monitoring, drift detection, and rollback capabilities
• Drive adoption of AI-assisted operations tooling to enhance observability, anomaly detection, and predictive incident management

Leadership & Culture

• Guide and mentor junior and mid-level SREs - conducting code reviews, running reliability reviews, and elevating the team’s engineering craft
• Collaborate cross-functionally with Data Engineering, Software Engineering, Security, and Product teams to embed reliability as a shared value from design through deployment
• Champion a culture of psychological safety, continuous learning, and reliability excellence
• Communicate platform health, risk posture, and reliability roadmaps clearly to both technical and executive audiences

• Master’s degree in Computer Science, Engineering, or related field (or equivalent practical experience)
• 10+ years of experience in Site Reliability Engineering, DevOps, or Platform Engineering, with a strong track record in large-scale production environments
• Deep, hands-on expertise in GCP - including GKE, Cloud Run, BigQuery, Pub/Sub, GCS, Composer, Dataflow, and Vertex AI
• Proficiency with Infrastructure-as-Code tools (Terraform, Helm) and GitOps workflows (ArgoCD, Flux)
• Strong command of observability tooling - distributed tracing, structured logging, metrics pipelines, and alerting platforms (e.g., Cloud Monitoring, Datadog, Prometheus/Grafana)
• Proven experience defining and operating against SLOs, SLIs, and error budgets in production environments
• Solid understanding of data security principles: IAM, encryption, secrets management, network policies, and compliance frameworks
• Experience with multi-cloud environments (GCP + Azure), including cross-cloud networking, identity federation, and cost governance
• Demonstrated ability to lead without authority - influencing engineers across teams and driving reliability improvements at the organizational level
• Excellent written and verbal communication skills; ability to translate complex reliability concepts for non-technical stakeholders

Technical Depth

• Fluency in at least one systems or scripting language (Python, Go, or Bash) for automation and tooling
• Experience with container orchestration (Kubernetes/GKE), service mesh, and traffic management patterns
• Familiarity with data engineering patterns: batch and streaming pipelines, data warehouses, and the operational challenges of large-scale data platforms
• Understanding of agentic AI architectures and the unique reliability challenges of LLM-based, event-driven, and multi-agent systems
• Working knowledge of data governance frameworks, data lineage tooling, and platform-level data quality enforcement

Desirable Experience

• Experience operating data platforms in retail or e-commerce environments
• Familiarity with SRE principles in practice - error budget policies, CRE engagements, production readiness reviews
• Exposure to FinOps practices - cloud cost attribution, commitment optimization, and unit economics for data workloads
• Experience with Azure-native services (AKS, Azure Data Factory, Event Hubs) and cross-cloud identity management
• Prior experience in a Staff or Principal-level SRE role with organization-wide scope