Senior Cloud Reliability Advocate

The Senior Site Reliability Engineer plays a pivotal role in ensuring the availability, latency, performance, and efficiency of our SaaS platform on Azure.

This individual will define and enforce reliability standards, lead high-impact projects, mentor engineers, and eliminate toil at scale. They will report directly to the Director of Site Reliability.

Key responsibilities include:

• Defining customer-centric Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for Tier-0/Tier-1 services.
• Implementing an error-budget policy with multi-window, multi-burn-rate alerts; clear runbooks and paging thresholds.
• Gating changes by budget status (freeze/relax rules) wired into CI/CD.
• Maintaining SLO/EB dashboards (Azure Monitor, Grafana/Prometheus, App Insights). Running weekly SLO reviews with engineering/product.
• Driving roadmap tradeoffs when budgets are at risk; landing reliability epics.
• Incident management without drama: Leading SEV1/SEV2 incidents, owning comms, running blameless postmortems, and making corrective actions stick.
• Engineering reliability in: Multi-AZ/region patterns (active-active/DR), PDBs/Pod Topology Spread, HPA/VPA/KEDA, resilient rollout/rollback.
• AKS at scale: Hardening clusters (network, identity, policy), optimizing node/pod density, ingress (AGIC/Nginx); mesh optional.
• Observability that works: Metrics/traces/logs with Azure Monitor/App Insights, Log Analytics, Prometheus/Grafana, OpenTelemetry. Alerting on symptoms, not noise.
• IaC & policy: Terraform/Bicep modules, GitOps (Flux/Argo), policy-as-code (Azure Policy/OPA Gatekeeper). No snowflakes.
• CI/CD reliability: Azure DevOps/GitHub Actions with canary/blue-green, progressive delivery, auto-rollback, Key Vault-backed secrets.
• Capacity & performance: Load testing, right-sizing, autoscaling; partnering with FinOps to reduce spend without hurting SLOs.
• Disaster Recovery you can trust: Defining RTO/RPO, testing backups/restore, running game days/chaos drills, validating ASR and multi-region failover.
• Secure by design: Entra ID (Azure AD), managed identities, Key Vault rotation, VNets/NSGs/Private Link, shift-left checks in CI.
• Reducing toil: Automating recurring ops, building self-service runbooks/chatops, publishing golden paths for product teams.
• Customer escalations: Being the technical owner on calls; communicating tradeoffs and recovery plans with authority.
• Documenting for scale: Architectures, runbooks, postmortems, SLIs/SLOs—kept current and discoverable.

Minimum Qualifications:

• Bachelor’s in Computer Science/Engineering (or equivalent experience).
• 12+ years in production ops/platform/SRE, including 5+ years on Azure.
• PostgreSQL (must-have): Deep operational expertise incl. HA/DR, logical/physical replication, performance tuning (indexes/EXPLAIN/ANALYZE, pg_stat_statements), autovacuum strategy, partitioning, backup/restore testing, and connection pooling (pgBouncer). Prefer experience with Azure Database for PostgreSQL – Flexible Server.
• Azure core: AKS (must-have); Front Door/App Gateway, API Management, VNets/NSGs/Private Link, Storage, Key Vault, Redis, Service Bus/Event Hubs.
• Observability: Azure Monitor/App Insights, Log Analytics, Prometheus/Grafana; SLO design and error-budget operations.
• IaC/automation: Terraform and/or Bicep; PowerShell and Python; GitOps (Flux/Argo). Pipelines in Azure DevOps or GitHub Actions.
• Proven incident leadership at scale, blameless postmortems, and SLO/error-budget governance with change gating.
• Mentorship and crisp written/verbal communication.

Preferred Qualifications:

• Apache NiFi, Apache Flink, Apache Kafka or Redpanda (self-managed on AKS or managed equivalents); schema management, exactly-once semantics, backpressure, dead-letter/replay patterns.
• Azure Solutions Architect Expert, CKA/CKAD.
• ITSM (ServiceNow), on-call tooling (PagerDuty/Opsgenie).
• Compliance/SecOps (SOC 2, ISO 27001), policy-as-code, workload identity.
• OpenTelemetry, eBPF tooling, or service mesh.
• Multi-tenant SaaS and cost optimization at scale.