Team Lead, Cloud FinOps

OneTrust•Atlanta, GA

7h•$108,750 - $163,125•Hybrid

About The Position

The SaaS FinOps Leader is responsible for establishing and operating the company’s cloud financial management discipline across Azure cloud infrastructure, AI token usage, and cloud cost optimization initiatives. This role partners with Engineering, Product, Finance, Procurement, Security, and Operations to ensure cloud and AI spend is governed, optimized, and aligned to business value. The ideal candidate combines cloud cost management expertise, SaaS operating model understanding, financial discipline, and strong cross-functional influence. This is not only a reporting role; it is an operating leadership role accountable for driving measurable improvements at enterprise scale.

Requirements

5+ years of experience with multi-tenant SaaS platforms and product-level cloud cost attribution.
Experience with Microsoft Azure cloud services
Experience with FinOps platforms such as CloudZero or similar tools.
Experience supporting enterprise SaaS gross margin improvement initiatives.

Responsibilities

Own the company’s Azure cloud cost management strategy, including forecasting, budgeting, spend tracking, and variance analysis.
Establish cloud cost governance practices across subscriptions, resource groups, environments, products, teams, and customer segments.
Partner with Finance to create accurate monthly, quarterly, and annual cloud spend forecasts.
Define and maintain cloud cost allocation models, including tagging standards, chargeback/showback, and product-level cost attribution.
Produce executive-level reports on cloud spend trends, risks, saving opportunities, and cost-to-serve performance.
Partner with Procurement and Finance on Azure commercial agreements, reserved capacity, committed-use discounts, marketplace purchases, and vendor negotiations.
Lead initiatives to reduce waste and improve Azure cost efficiency across compute, storage, databases, networking, observability, backup, disaster recovery, and development environments.
Identify and drive optimization opportunities such as: Right-sizing underutilized resources, Eliminating idle or orphaned infrastructure, Improving autoscaling and workload scheduling, Increasing use of reservations, savings plans, and spot capacity where appropriate, Rationalizing non-production environments.
Partner with Engineering and Architecture to embed cost optimization into platform design, service design, and deployment practices.
Establish cost guardrails for new services, environments, and major architecture changes.
Drive accountability for cost efficiency without compromising reliability, security, scalability, or customer experience.
Own financial governance for AI model usage, including token consumption, model selection, usage monitoring, budgeting, and optimization.
Create visibility into AI-spend across teams, use cases, tools, models, environments, and products.
Partner with Engineering, Product, and R&D Operations to define AI usage policies and cost controls.
Identify opportunities to optimize AI costs through: Model routing and model tiering, Prompt optimization, Caching and reuse, Batch processing, Rate limits and quotas, Usage-based budgets, Guardrails for experimentation versus production workloads.
Establish KPIs for AI cost efficiency, such as cost per workflow, cost per customer interaction, cost per engineering task, cost per generated artifact, or cost per automated transaction.
Articulate business cases for AI investments by connecting token spend to productivity, product value, customer adoption, or operational leverage.