Principal Software Engineering Reliability and FinOps Engineer

What you'll do

• Provide technical leadership for secure, scalable, and cost-efficient platforms across Azure, AWS, Kubernetes, and hybrid-cloud environments.
• Lead FinOps initiatives including cost allocation, anomaly detection, forecasting, rightsizing, and validated savings realization.
• Develop policy-driven optimization workflows that move recommendations through engagement, approval, execution, and reporting.
• Apply an automation-by-default approach using Python, PowerShell, Bash, and REST APIs to reduce manual operational toil.
• Design generative AI and agentic automation solutions for engineering operations, cost analysis, and executive reporting.
• Establish cloud-governance guardrails, policy-as-code controls, and risk-based automation patterns aligned with security requirements.
• Engineer secure identity and authorization models using role-based access control and privileged identity management.
• Advance reliability engineering practices including observability, service-level objectives, capacity management, and incident prevention.

What we're looking for

• 7+ years of progressive experience in systems engineering, cloud engineering, site reliability engineering, platform engineering, DevOps, FinOps, or enterprise automation.
• Deep hands-on experience with Azure and/or AWS architecture, networking, compute, storage, identity, monitoring, governance, and cost-management services.
• Strong knowledge of FinOps practices including allocation, tagging, budgeting, forecasting, unit economics, commitment management, and savings validation.
• Advanced scripting and automation experience using Python, PowerShell, Bash, REST APIs, JSON, and event-driven integration patterns.
• Strong Infrastructure as Code and CI/CD experience using Terraform, GitHub Actions, Azure DevOps, or comparable enterprise tooling.
• Hands-on experience with Kubernetes, AKS, EKS, or OpenShift, including workload rightsizing, Helm, observability, and container cost allocation.
• Experience applying generative AI, prompt engineering, AI assistants, or agentic automation to engineering and operational use cases.
• Bachelor's or master's degree in Computer Science, Computer Engineering, Information Systems, Data Science, or Finance (preferred).