FinOps Analyst, Platform Cloud
$120k - $140kAcadian Asset Management LLC
Acadian Asset Management is a global, systematic investment manager at the forefront of data-driven investing since 1986. Headquartered in Boston, with locations in Singapore, London, and Sydney, we manage over $195 billion on behalf of leading institutions worldwide—including pension funds, endowments, foundations, and sovereign wealth funds. We harness advanced technology, rich datasets, and multidisciplinary expertise to help clients navigate complex markets and uncover insights that may be overlooked by traditional approaches. What sets Acadian apart is our people. We foster a collaborative, intellectually curious environment where ideas are tested, diverse perspectives are welcomed, and innovation thrives. We’re united by a shared purpose: delivering effective client outcomes and supporting one another in work that’s both challenging and rewarding. We offer a flexible hybrid work environment, strong benefits, and a casual but focused office culture—all designed to support the meaningful, collaborative work that defines Acadian. Position Overview: The Platform team is responsible for building and supporting services that enable our development teams to leverage frameworks, tooling, and services across in-house and vendor PaaS/IaaS offerings. Platform defines the architectural roadmap, best practices, and provides education on these services. This role will focus on Cloud Financial Operations (FinOps), partnering across Platform, Investment, and Engineering teams to drive cost transparency, accountability, and optimization across Acadian’s cloud footprint. You will play a key role in managing and optimizing a multi-million dollar cloud budget spanning AWS and Kubernetes environments, while enabling teams to make informed, cost-efficient decisions. You will help establish governance, forecasting, and cost optimization practices, ensuring that cloud investments align with business value and performance requirements. Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week. What You’ll Do:
- Own and manage cloud cost visibility, reporting, and governance across AWS, Kubernetes (EKS), and SaaS providers
- Develop and maintain financial models for cloud capacity planning, demand forecasting, and budgeting aligned with Finance, IT, and Investment leadership
- Establish and monitor unit economics and cost allocation models
- Analyze and optimize spend across services including EC2, EKS, S3, Lambda, and AI/ML platforms ensuring cost controls and efficiency in all workloads
- Partner with engineering teams to implement cost-aware architecture patterns and and drive adoption of FinOps principles across the organization
- Build dashboards and reporting using Tableau, AWS Cost and Usage Reports (CUR), and Python-based analytics
- Evaluate and implement tooling for automated cloud cost governance and optimization
- Identify and mitigate cost risks, including anomalies and inefficient resource utilization
- Provide regular reporting and insights to leadership on spend trends and forecasts
- BS in Computer Science, Finance, Engineering, or related field preferred with 5+ years of relevant experience
- Proven experience managing and optimizing multi-million dollar cloud budgets in AWS
- Strong knowledge of AWS cost management tools
- Strong knowledge of AWS cost saving programs (PPA, rate card, savings plans, marketplace spend)
- Experience with Kubernetes cost management in production environments
- Demonstrated experience in capacity planning, demand forecasting, and financial modeling
- Proficiency in Tableau, Python, and SQL
- Familiarity with cost allocation strategies
- Understanding of modern cloud architectures and cost implications
- Experience with AI/ML platforms and cost optimization strategies
- Strong analytical mindset with ability to translate data into insights
- Experience collaborating with engineering and finance teams
- Familiarity with infrastructure tooling is a plus
- Excellent communication skills
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