Accountabilities:

Drive cloud and AI cost optimization initiatives across AWS and Google Cloud Platform (GCP), identifying, quantifying, and implementing savings opportunities across compute, storage, networking, and data services.
Monitor and analyze AI infrastructure and model usage costs across multiple providers, including Anthropic, OpenAI, and other large language model (LLM) platforms.
Design, implement, and automate financial governance guardrails, including budget alerts, anomaly detection, resource rightsizing recommendations, and automated cost-control mechanisms.
Develop and enforce cloud tagging and cost-allocation frameworks to ensure accurate cost attribution, showback and chargeback reporting, budgeting, forecasting, and unit economics analysis.
Manage cloud commitment-based discount programs, including Committed Use Discounts, Savings Plans, and Reserved Instances, by analyzing usage patterns and optimizing coverage and utilization.
Partner closely with engineering teams to assess cloud architectures, identify inefficiencies, and recommend cost-effective infrastructure designs while embedding FinOps best practices into development and deployment workflows.
Build and maintain dashboards, financial reports, and forecasting models that provide stakeholders with clear visibility into spending trends, cost drivers, and the return on investment of optimization initiatives.
Oversee third-party SaaS and technology spending, collaborating with engineering and procurement teams to evaluate vendors, optimize subscriptions, and identify consolidation opportunities.
Establish and promote FinOps standards, best practices, and self-service tools that encourage financial accountability and cost awareness across the organization.
Continuously improve cost-management processes through automation, data analysis, and scalable solutions that support long-term infrastructure efficiency.

Requirements

At least 3 years of professional experience in cloud engineering, infrastructure engineering, DevOps, or FinOps, with a strong focus on cloud cost optimization and financial governance.
Hands-on expertise with at least one major cloud provider, AWS or GCP, with experience managing cost optimization in multi-cloud environments considered a strong advantage.
Solid understanding of cloud architecture, infrastructure services, and pricing models across compute, storage, networking, and managed services.
Proven experience developing and managing automated FinOps alerting systems, including anomaly detection, budget thresholds, forecasting alerts, and alert routing and optimization.
Familiarity with tracking AI model usage and expenditure across multiple LLM providers, including token-based pricing models, provider usage and billing APIs, and cost attribution across teams or products.
Proficiency with cloud cost-management tools and platforms, such as GCP Billing and BigQuery billing exports, AWS Cost Explorer, AWS Cost and Usage Reports (CUR), CloudHealth, or equivalent solutions.
Strong SQL skills and proficiency in at least one scripting language, preferably Python, for data analysis, reporting, and workflow automation.
Experience developing dashboards and financial reports using Looker, Looker Studio, Tableau, or similar business intelligence platforms.
Strong understanding of financial and engineering concepts, with the ability to translate technical architecture decisions into financial impact and convert cost objectives into actionable technical improvements.
Excellent analytical and problem-solving skills, including the ability to work with large datasets, identify trends, quantify savings opportunities, and prioritize initiatives based on business impact.
Strong communication and stakeholder management skills, with the ability to influence engineering, finance, and procurement teams without direct authority.
A proactive, automation-oriented mindset, with a preference for building scalable, repeatable solutions rather than relying on manual processes.
Ability to communicate complex technical and financial information clearly to both technical and non-technical audiences.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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