Senior Cloud FinOps engineer

📋 Job Overview

The Senior Cloud FinOps Engineer leads complex cloud financial management, optimization, and governance activities across multiple managed services clients. This role combines deep public cloud engineering knowledge, financial and operational analysis, customer-facing advisory capability, and execution discipline to help clients improve visibility, accountability, forecasting accuracy, unit economics, and cost efficiency across AWS, Azure, and GCP.

🏢 About AHEAD

AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation.

🎯 The Role

As a senior member of the practice, this engineer is a recognized subject matter expert who owns higher-complexity analyses, leads written deliverables and client-facing advisory discussions, mentors other engineers, and drives improvements to standards, automation, and repeatable delivery practices across the FinOps service offering. The role operates with a high degree of commercial awareness and mature communication skills, engaging service owners, process owners, finance and procurement stakeholders, and executive audiences directly.

✅ Key Responsibilities

• Lead detailed analysis of cloud spend, usage trends, allocation accuracy, anomalies, and optimization opportunities across AWS, Azure, and GCP using native tooling and approved third-party FinOps platforms.
• Identify, quantify, prioritize, and drive optimization opportunities including rightsizing, idle resource cleanup, storage and data transfer optimization, licensing alignment, and scheduling strategies.
• Track optimization opportunities through identified, recommended, approved, implemented, and realized states, validating financial outcomes and avoiding double-counting or attribution of unrealized savings.
• Lead cost visibility and allocation for containerized and Kubernetes workloads, including shared cluster cost distribution, namespace and label-based allocation, and idle capacity analysis.
• Analyze and optimize AI and GPU-related spend, including accelerated compute utilization, model training and inference costs, and consumption of managed AI and machine learning services.
• Extend cost analysis to SaaS, software licensing, and marketplace spend where these fall within the agreed scope of the engagement.
• Conduct best-practices reviews that combine cost analysis with operational excellence analysis, using cloud-native and approved AHEAD tooling.

🎁 Benefits

Competitive

🛂 Visa & Eligibility

The role is performed remotely within the United States.