Lead Site Reliability Engineer

What You'll Do:
Role Summary:
As Avalara continues to scale its global SaaS platform and accelerate toward an AI-first operating model, we must fundamentally transform how reliability, deployment, and operational excellence are engineered.
This role exists to design and lead an enterprise-grade, AI-driven reliability ecosystem, enabling self-healing systems, intelligent observability, and low-risk deployment practices across multi-cloud environments.
This includes modernizing RELE practices through automation, feature flag-driven deployments, and AI-powered operational workflows.
This is a high-impact individual contributor role responsible for reducing operational risk, eliminating manual toil, improving system resilience, and enabling faster, safer product delivery at scale.
How This Role Elevates Avalara:
This role strengthens Avalara's reliability engineering and platform operations capability by introducing AI-driven, automation-first reliability practices.
This Lead Reliability Engineer will:
- Improve platform stability and reduce incidents through AIOps, predictive monitoring, and self-healing systems
- Accelerate deployment velocity and reduce risk through progressive delivery, feature flag strategies, and CI/CD optimization
- Enhance customer experience by improving availability, performance, and recovery times
- Increase operational efficiency by eliminating manual processes and introducing intelligent automation workflows
- Advance Avalara's AI-first strategy by embedding agentic AI into observability, incident response, and reliability engineering
What Your Responsibilities Will Be:
Bar Raiser Expectations:
As a Bar Raiser, this role is expected to elevate the performance of the entire reliability engineering function:
- Hold high standards for availability, reliability, automation, and operational excellence
- Use metrics such as MTTR, SLI/SLO/SLA adherence, deployment success rate, and incident reduction to drive decisions
- Simplify complex distributed systems into scalable, resilient, and automated platforms
- Mentor engineers and raise technical rigor, automation maturity, and AI adoption
- Challenge assumptions and drive measurable improvements in system reliability and deployment safety
- Leave every system, process, and platform more resilient and scalable than before
This role does not just operate systems—it redefines how reliability is engineered at scale.
Reliability Engineering & Platform Leadership:
- Own the end-to-end reliability strategy for distributed SaaS systems across multi-cloud environments
- Design and implement AI-driven operations (AIOps) including anomaly detection, predictive failure analysis, and automated root cause identification
- Build and scale observability platforms using Prometheus, Grafana, OpenTelemetry, and ML-based analytics
- Architect self-healing systems and automation frameworks to eliminate manual operational toil
- Lead modernization of deployment practices through feature flags, progressive delivery, and safe rollout strategies
- Drive reliability improvements across Kubernetes-based container platforms
Platform Ownership & Deployment Engineering:
- Own reliability of CI/CD pipelines and infrastructure as code (Terraform/Pulumi)
- Design deployment strategies that reduce risk, including:
- Feature flag-based releases
- Canary and progressive rollout models
- Automated rollback and kill-switch capabilities
- Improve deployment observability and traceability across environments
- Ensure high availability, scalability, and fault tolerance of production systems
Observability, Automation & AI Integration:
- Implement advanced monitoring, logging, and tracing systems across services
- Integrate agentic AI workflows into incident detection, triage, and resolution
- Build automation pipelines using Go, Python, and modern workflow tools
- Enable AI-assisted observability, including:
- Intelligent alerting
- Automated diagnostics
- Performance optimization insights
- Drive adoption of automation-first and AI-first operational practices
Operational Excellence & Incident Management:
- Lead incident response and on-call readiness for production systems
- Improve incident resolution time and system recovery through automation
- Conduct post-incident reviews and implement systemic improvements
- Communicate clearly with stakeholders and customers during incidents
12-Month Success Signals:
Within the first 12 months, this role will have:
- Reduced MTTR by *% through automation and AI-driven diagnostics
- Decreased production incidents and customer impact events
- Implemented AI-driven observability and alerting systems across core platforms
- Enabled feature flag-based deployment strategies acros