Lead Cloud Economics Specialist

The Role

The Lead Cloud Economics Specialist will own analytics, automation, and operating processes for cloud cost management, cost attribution, optimization tracking, strategic vendor economics, and product-margin support.

The role will partner with Finance, Product & Engineering, IT, Procurement, Legal/Trust, and Data teams to turn complex usage and cost data into clear financial insights, scalable reporting, and executive-ready recommendations.

This is a senior, hands-on finance business partnering role at the intersection of Finance, cloud economics, data automation, vendor economics, and Product & Engineering decision support.

You will work with complex usage and cost data, build scalable reporting and automation, and translate technical cost drivers into clear financial insights and recommendations.

You will lead through ownership and influence rather than direct management, and you\'ll have room to define structure where mature processes don\'t yet exist.

• The work you’ll do
• Cloud cost visibility and attribution
• Build and maintain reporting across AWS, Azure, GCP, and related hosting environments.
• Improve attribution of cloud and infrastructure costs to services, teams, products, customers, projects, and cost centers.
• Automation, data pipelines, and analytics
• Use Python or equivalent scripting, SQL, APIs, BI tools, and workflow automation to reduce manual reporting.
• Build and maintain data pipelines connecting cloud billing, vendor usage, employee/project mapping, product usage, and financial data.
• Optimization, forecasting, and vendor economics
• Maintain an optimization pipeline with opportunity size, owner, expected savings, realized savings, and implementation status.
• Support cloud commitment, reservation, savings-plan, and renewal decisions with usage baselines, forecast scenarios, and risk analysis.
• Track strategic vendor spend and usage for major platforms such as Datadog, Cloudflare, Databricks, Cursor, Git Hub Copilot, Lite LLM-related usage, Open AI-related usage, and other tools.
• Business partnering and decision support
• Act as a finance partner to Product & Engineering teams on cloud, infrastructure, software, and AI-related technology spend.
• Translate cost and usage trends into practical recommendations for budget owners, Finance, Procurement, and leadership.
• Support selected product, customer, or unit-cost analyses where data is available and the business decision requires it.
• Help connect technology spend insights to planning, prioritization, and margin discussions without creating a separate product P&L ownership model.
• R&D investment and executive reporting support
• Support Product & Engineering planning cycles with analysis of major technology spend drivers.
• Contribute to lightweight R&D investment analysis where employee, project, usage, and financial data is available.
• Create recurring executive-ready materials covering savings, risks, forecasts, margin impact, open decisions, and recommended actions.
• First-Year Success Measures

Success in the first 12 months will include

• Automated recurring reporting for core cloud and strategic technology spend.
• Improved attribution coverage for cloud and software costs.
• A maintained optimization pipeline and visibility into savings opportunities.
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