Staff Site Reliability Engineer, FinOps

sumologic; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote work in Costa Rica

Optional office access in San José

Job summary

Sumo Logic is seeking a senior Cost Optimization Analyst who uses quantitative methods to forecast cloud and AI spend, analyze unit economics, and assess commitment options. The role partners with Engineering, Finance, and executives to explain assumptions and drive decisions.

The position is fully remote in Costa Rica with optional office access in San José. The candidate brings 7+ years in data or software engineering and at least 2 years in FinOps, plus experience building scalable data

Qualifications

• 7+ years of professional data or software engineering experience.
• At least 2 years in FinOps or cloud financial management.
• Experience building and operating data pipelines from complex sources.
• Experience partnering with Finance/FP&A on budgets, forecasts, or gross margin analysis.
• Operational experience with large-scale, global multi-tenant SaaS services.
• Strong quantitative reasoning and communication skills to defend modeling assumptions.

Responsibilities

• Build forecasts and financial models for cloud and AI spending, including unit economics and sceniarios.
• Evaluate cloud commitment options by weighing potential savings against utilization risk.
• Develop cost allocation methods that connect spending to teams and products.
• Operate and improve cost regression tools and programs; investigate increases and help teams resolve them.
• Lead cost optimization work across teams and connect engineering, finance, and executive decision-makers.
• Run cost data pipelines as production services with monitoring and data quality checks.

Skills

Data pipelines

Forecasting

Quantitative analysis

Cross-functional collaboration

Communication

Education

Bachelor's or Master's in CS/engineering/finance

Doctorate preferred

Tools

Python

Scala

Java

Go

Job description

Role overview

Apply site reliability engineering practices to cloud and AI cost management, building the models and operational systems that help teams understand and optimize spend. This is a quantitatively focused role: you will forecast costs, analyze unit economics, assess commitment options, and explain the assumptions behind recommendations to Engineering, Finance, and senior stakeholders. The position is listed as fully remote in Costa Rica, with optional office access in San José.

Responsibilities

• Build forecasts and financial models for cloud and AI spending, including unit economics, cost of goods sold, and scenario and sensitivity analysis.
• Evaluate cloud commitment options by weighing potential savings against utilization risk.
• Develop cost allocation methods that connect spending to the teams and products driving it.
• Operate and improve cost regression tools and programs; investigate increases and help teams resolve them.
• Lead cost optimization work across teams and connect engineering, finance, and executive decision-makers.
• Run cost data pipelines as production services, with monitoring, alerting, data quality checks, automation, and regular operational review.

Requirements

• Bachelor’s or master’s degree in computer science, engineering, finance, or a related field, or comparable expertise; a doctorate is a plus.
• At least 7 years of professional data or software engineering experience and at least 2 years in FinOps or cloud financial management.
• Strong ability to build and operate data pipelines using complex data sources, and to code and debug effectively.
• Experience partnering with Finance or FP&A on budgets, forecasts, or gross margin analysis.
• Operational experience with large-scale, highly available, global multi-tenant SaaS services.
• Strong quantitative reasoning and communication skills, including the ability to defend modeling assumptions and turn analysis into decisions.

Nice to have

• Experience with Scala, Java, Go, or Python; Scala is particularly useful.
• Relevant education at the doctoral level.