Lead Cloud Economics Specialist

Celonis; München; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

PVH (Tommy Hilfiger/Calvin Klein) seeks a Lead Cloud Economics Specialist to own analytics, automation, and operating processes for cloud cost management, attribution, and optimization tracking. You’ll partner with Finance, Product & Engineering, IT, Procurement and Data teams to convert usage data into actionable financial insights and scalable reporting.

The role emphasizes building pipelines, forecasting, and executive-ready recommendations to guide budgeting and vendor decisions across major

Qualifikationen

• 6–10+ years in Finance, Cloud Economics, FP&A, or related analytical roles.
• Strong analytical skills and large data handling capability.
• Hands-on Python or similar scripting for data analysis and automation.
• Strong SQL skills and BI tool experience for reporting and forecasting.

Aufgaben

• Own cloud cost visibility, attribution, and reporting across providers.
• Build data pipelines connecting cloud billing, usage, and financial data.
• Maintain optimization pipelines with expected and realized savings.
• Partner with Product & Engineering on cloud, infra, and AI spend.
• Create executive-ready materials on savings, risks, and actions.

Kenntnisse

Financial analysis

Cloud economics

FP&A

Data analytics

Business operations

Tools

Python

SQL

BI tools

APIs

Jobbeschreibung

Lead Cloud Economics Specialist

Role Overview:

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.

Responsibilities

• 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, GitHub Copilot, LiteLLM‑related usage, OpenAI‑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

• Automated recurring reporting for core cloud and strategic technology spend.
• Improved attribution coverage for cloud and software costs.
• A maintained optimization pipeline with expected and realized savings.
• Faster detection and investigation of cost anomalies.
• Clearer renewal and commitment recommendations for major vendors.
• Established finance business partnering cadence with Product & Engineering stakeholders.
• Targeted product, customer, or unit‑cost analysis for selected decisions where data is available.
• Regular executive visibility into technology spend, risks, savings opportunities, and business impact.

Qualifications

• 6–10+ years of experience in Finance, Cloud Economics, FP&A, Strategic Finance, Business Operations, Engineering Operations, Data Analytics, or a related analytical role.
• Strong analytical skills, including advanced spreadsheet modeling and experience working with large datasets.
• Hands‑on experience with Python or an equivalent scripting language for data analysis, automation, and reporting.
• Strong SQL skills and experience with BI tools, APIs, cloud billing data, or recurring data pipelines.
• Experience analyzing SaaS, infrastructure, cloud, software, or usage‑based cost models.
• Strong business judgment and the ability to translate technical usage data into clear financial insights and actionable recommendations.