FinOps Engineer III

The Infrastructure Engineering group (IE) at ZoomInfo is responsible for the innovation, scaling, and automation that powers our global platforms. Within this group, the Cloud Financial Governance function serves as the strategic architect of our technology investment.

 We are seeking a FinOps Engineer to join the centralized FinOps team of our enterprise-scale, multi-cloud organization. In this role, you will play a crucial part in optimizing our core GCP and AWS spend, as well as our global suite of third-party tech stacks by driving financial accountability and implementing best practices. Getting hands-on with our infrastructure, you will identify, build, and automate cost-saving opportunities. You will work directly with engineering teams to optimize cloud architectures, implement robust tagging frameworks, and deploy the automated guardrails needed to keep our environments highly efficient

What You'll do:

• Drive cloud and AI cost optimization across GCP and AWS by identifying, quantifying, and implementing savings opportunities spanning compute, storage, networking, and data services. Track AI usage and costs across multiple providers such as Anthrophic, OpenAI, and others.
• Build and automate guardrails - including budget alerts, anomaly detection, and rightsizing recommendations to keep multi-cloud environments efficient by default.
• Design and enforce tagging and allocation frameworks that enable accurate cost attribution, showback/chargeback, budgeting & forecasting, and unit-economics reporting across teams and products.
• Manage commitment-based discounts (Committed Use Discounts, Savings Plans, Reserved Instances) by analyzing usage patterns and optimizing coverage and utilization.
• Partner directly with engineering teams to review and re-architect workloads for cost efficiency, embedding FinOps principles into design and deployment practices.
• Develop dashboards, reports, and forecasts that give stakeholders clear visibility into spend trends, drivers, and the ROI of optimization efforts.
• Govern third-party SaaS and tech-stack spend, partnering with engineering to evaluate, right-size, and consolidate vendor costs.
• Establish and evangelize FinOps best practices, standards, and self-service tooling that scale financial accountability across the organization.

What you bring:

• 3+ years of experience in cloud engineering, infrastructure, DevOps, or FinOps, with a strong focus on cost optimization and governance.
• Hands-on expertise with at least one major cloud provider (GCP or AWS); experience with both in a multi-cloud environment strongly preferred. Solid understanding of cloud architecture, services, and pricing models across compute, storage, networking, and managed services.
• Proven experience building and managing automated FinOps alerting: anomaly detection, budget and forecast thresholds, and alert routing and tuning
• Familiarity with tracking AI model usage and spend across multiple LLM providers, including token-based pricing, provider usage and billing APIs, and attribution of AI cost to teams or productsProficiency with cloud cost-management tooling (e.g., GCP Billing/BigQuery billing exports, AWS Cost Explorer/CUR, CloudHealth, or equivalent third-party platforms).
• Proficiency in SQL and at least one scripting language (Python preferred) for data analysis and automation
• Experience building dashboards and reports in Looker, Looker Studio, Tableau or similar tools
• Clear communication skills, and the ability to influence engineering and finance stakeholders without direct authority
• FinOps Certified Practitioner certification and/or relevant cloud certifications are a plus.
• Financial and engineering fluency - able to translate technical architecture decisions into financial impact, and financial goals into technical action.
• Analytical rigor - comfortable working with large datasets to surface insights, quantify opportunities, and prioritize by impact.
• Strong cross-functional collaboration - able to influence engineering, finance, and procurement stakeholders without direct authority.
• Bias for automation - instinctively builds repeatable, scalable solutions rather than one-off manual fixes.
• Clear communication - able to present complex cost data and recommendations to both technical and non-technical teams.

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