Sr FinOps Data Analyst

Hearst•Charlotte, NC

3d•$102,500 - $125,000

About The Position

Hearst Technology Services, Inc. (HTS) is hiring a cloud-focused Senior FinOps Data Analyst to analyze cloud costs and resource consumption across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) IaaS and PaaS environments. The role supports approximately 50 subsidiary business units, maintains cloud billing data and front-end cost management tools, and presents monthly consumption insights and optimization opportunities to internal businesses. The position reports to the Senior FinOps Manager of Hearst’s Cloud Center of Excellence. The analyst will also track and analyze AI/ML costs, including GPU and accelerator utilization, model training and inference, and third-party LLM API usage. The role works with Finance, engineering, data science, business units, and leadership on cost visibility, allocation, forecasting, optimization, and FinOps practices.

Requirements

At least 6 years of experience in FinOps, cloud cost management, financial analysis, or a related analytical role.
Proficiency in SQL for querying, transforming, and analyzing large datasets.
Hands-on experience with cloud cost management or optimization tools such as CloudHealth (VMware Aria Cost), Cloudability, or native cloud billing tools.
Solid understanding of cloud provider billing models, including reserved instances, savings plans, spot pricing, and committed use discounts.
Experience building business intelligence reports and dashboards using SQL-based BI tools, Excel, Tableau, Power BI, or Looker.
Strong analytical and problem-solving skills, with high attention to detail.
Excellent communication skills and ability to translate technical cost data into business-relevant insights.
Familiarity with cloud tagging strategies and cost allocation frameworks.
Working knowledge of AI/ML cost drivers, including GPU and compute pricing, token- or request-based LLM API billing, and cost differences across model providers and tiers.
BS/BA degree in Computer Science, Engineering, Mathematics, or relevant industry experience.

Nice To Haves

Advanced Microsoft Excel skills, including Pivot Tables and complex formulas.
Knowledge of FinOps principles; FinOps Foundation certification is a plus.
Experience monitoring AI/LLM usage and cost through platforms such as Anthropic Console, OpenAI usage dashboards, Azure AI cost management, or third-party AI cost tools.
Experience with business intelligence tools such as Power BI or Tableau.
Strong statistics and algebra skills.
Prior experience working directly with engineering teams on cost-aware architecture decisions.
Ability to read or write scripts in one or more modern scripting or programming languages, such as R, SQL, Python, Scala, Java, C++, Ruby, PowerShell, or Go.
Cloud certifications.
FinOps Certified Practitioner (FOCP) certification, or willingness to obtain it.

Responsibilities

Monitor, analyze, and report on cloud spend across AWS, Azure, and Google Cloud environments.
Lead monthly cloud consumption and cost optimization business review meetings with Hearst-owned businesses.
Build and maintain dashboards and reports using SQL and cost management platforms to track spend trends, budget variance, and forecast accuracy.
Identify cost optimization opportunities, including rightsizing, reserved instance and savings plan coverage, idle resource elimination, and storage tiering.
Partner with engineering teams to implement tagging strategies, cost allocation models, and showback or chargeback frameworks.
Support monthly and quarterly cloud spend reviews with business units and leadership.
Investigate cost anomalies and spend spikes, identify root causes, and recommend corrective action.
Maintain and improve cost allocation logic, including tagging compliance and reduction of unallocated spend.
Collaborate with Finance on cloud cost budgeting, forecasting, and variance analysis.
Assist in developing and refining FinOps policies, KPIs, and maturity roadmaps.
Stay current on cloud provider pricing models, discount programs, and cost management tooling.
Track and analyze AI/ML costs, including GPU and accelerator utilization, model training and inference spend, and third-party LLM API usage.
Build visibility into AI cost drivers such as token consumption, request volume, model selection, and reserved versus on-demand compute usage.
Partner with data science and engineering teams to right-size GPU instances, evaluate reserved versus spot capacity for training jobs, and identify underutilized AI infrastructure.
Support forecasting and budgeting for AI initiatives, accounting for the variable and rapidly scaling nature of AI/ML spend.