Staff Site Reliability Engineer, FinOps

FinOps Weekly; Toronto; Remote; CAD 196,000 - 265,000; Full time

Job summary

FinOps Weekly is seeking a Staff Site Reliability Engineer to join its FinOps team. The role focuses on applying SRE discipline to cloud cost management, building financial models, and leading cost optimization in a large SaaS environment.

The position requires strong coding, data modeling, and cross-functional collaboration with Engineering, Finance/FP&A, and leadership. Remote-friendly with Costa Rica option.

Qualifications

• Bachelor's or Master's degree in CS/Engineering/Finance or related field.
• 7+ years in data or software engineering.
• 2+ years cloud financial management or FinOps.
• Experience building and operating data pipelines with complex sources.
• Experience with budgeting, forecasting, or gross-margin analysis.
• Experience in high-scale, reliable SaaS environments.
• Strong coding and debugging skills.
• Proficient in Scala, Java, Golang, or Python.
• Excellent communication to explain complex data to stakeholders.

Responsibilities

• Build financial models for cloud and AI spend with unit economics and forecasting.
• Model commitment strategies across CSP, RI, and EDP.
• Develop cost allocation and attribution methods for spend visibility.
• Maintain and improve cost regression detection and response processes.
• Lead cost optimization initiatives across teams.
• Automate reporting, analysis, and FinOps workflows.
• Improve cost alerting and thresholds using SRE practices.
• Quantify tradeoffs between cost optimization and reliability.

Skills

Coding skills

Scala experience

Java experience

Golang experience

Python experience

Communication skills

Data insights

Education

Bachelor's or Master's in CS/Engineering/Finance

PhD is a plus

Job description

Staff Site Reliability Engineer, FinOps

Location: 100% Remote — Costa Rica (optional office access in San José)

About the Role

This role sits within Sumo Logic’s FinOps team and focuses on making cloud and AI spend visible, attributable, and optimizable. The position applies SRE principles such as measurement, observability, automation, and accountability to cloud cost management, with an emphasis on treating cost with the same rigor used for reliability.

The Staff SRE will build the financial models that support cost decisions, including spend forecasts, unit economics, COGS, and commitment planning. The role works closely with Engineering, Finance/FP&A, and executive leadership to turn analysis into action.

This is a highly quantitative position that requires comfort with math, financial modeling, and statistical modeling. The successful candidate will create forecasts, sensitivity analyses, and optimization models, then defend assumptions and findings with stakeholders.

Key Responsibilities

• Build financial models for cloud and AI spend, including unit economics, COGS, and forecasting with scenario and sensitivity analysis.
• Model commitment strategies across CSP, RI, and EDP by balancing savings against utilization risk.
• Design cost allocation and attribution approaches so spend can be mapped to the teams and products driving it.
• Own and improve the cost regression program and related tooling to detect, investigate, and help resolve spend increases.
• Lead cost optimization efforts across teams and help shape the broader cost optimization roadmap.
• Serve as a bridge between engineering, finance, and executive leadership on FinOps priorities.
• Improve operational excellence across FinOps pipelines and tools.
• Run cost data pipelines and tooling with production-grade practices, including monitoring, alerting, data quality checks, and automation.
• Apply SRE-style practices to cost management, including budget and threshold design, cost alerting, and blameless reviews of major regressions.
• Quantify cost-versus-reliability tradeoffs to avoid harming customer experience through optimization decisions.
• Automate manual cost reporting and analysis work.
• Contribute cost insights to the weekly LiveSite operational review.
• Mentor and support junior team members.

Required Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, Finance, or a related field.
• Ph.D. is a plus.
• 7+ years of professional experience in data or software engineering roles.
• 2+ years of experience with cloud financial management or the FinOps framework.
• Demonstrated ability to build and operate data pipelines with complex data sources.
• Experience working with Finance/FP&A on budgeting, forecasting, or gross-margin analysis.
• Experience in high-scale, reliable, 24/7, multi-tenant global SaaS environments.
• Strong coding and debugging skills.
• Experience with Scala, Java, Golang, or Python.
• Strong analytical and problem-solving ability.
• Ability to translate complex data into actionable insights.
• Proven ownership and ability to influence outcomes across internal and external organizations.
• Excellent communication skills, including the ability to explain technical concepts to non-technical stakeholders.

FinOps Responsibilities

• Build forecasts, unit economics models, and COGS analysis for cloud and AI spend.
• Develop scenario and sensitivity analyses to support financial planning.
• Shape commitment strategy across CSP, RI, and EDP.
• Create cost allocation and attribution methods for spend visibility.
• Maintain and improve cost regression detection and response processes.
• Lead cost optimization initiatives across teams.
• Automate reporting, analysis, and FinOps workflows.
• Improve cost alerting, thresholds, and review processes using SRE practices.
• Quantify tradeoffs between cost optimization and reliability.

Why You Might Be Interested

This role offers the opportunity to apply SRE discipline to a FinOps function with direct influence on cloud and AI spending decisions. It combines technical depth, financial modeling, and cross-functional partnership with Engineering, Finance/FP&A, and executive leadership. The position is well suited to someone who enjoys solving quantitative problems, improving operational systems, and driving measurable cost optimization in a large SaaS environment.