## About the Role

At Plaid, the FinOps function is responsible for financial accountability, visibility, and optimization across engineering-related spend. That includes cloud infrastructure, AI/ML, data workloads, SaaS tools, and other technical investments.

The team works across Engineering, Product, Finance, and TPMs to make cost decisions clear, intentional, and aligned with business priorities. Rather than acting as a control layer, FinOps helps teams own their spend while maintaining engineering speed.

This role focuses on analysis, forecasting, reporting, and process improvement so leaders and engineers can make better tradeoffs. The goal is to improve spend visibility, strengthen financial planning, and support more efficient use of technical resources.

## Key Responsibilities

– Monitor and analyze spend across cloud, AI/ML, data platforms, and SaaS, identifying trends, anomalies, and opportunities to optimize.
– Build and maintain forecasts for engineering spend in partnership with Finance and engineering leadership.
– Work with Engineering, Product, and TPMs to incorporate cost considerations into roadmaps, architecture, and execution plans.
– Lead optimization efforts such as rightsizing, commitment strategies, and workload efficiency improvements.
– Develop dashboards and reporting that make spend clear and actionable for engineers and executives.
– Define and support FinOps practices such as showback/chargeback, unit economics, and cost ownership frameworks.
– Partner with data and engineering teams on tooling and automation to improve visibility, forecasting accuracy, and operational efficiency.
– Help teams balance cost, performance, reliability, and velocity through data-informed decision-making.

## Required Skills

– 6–10+ years of relevant experience in engineering, infrastructure, data, or finance within a cloud-native or SaaS environment.
– Proven success partnering closely with engineering teams on cloud infrastructure, data platforms, AI/ML workloads, or SaaS spend.
– Experience managing AI and machine learning spend, including compute, model inference, API usage, budget forecasting, and optimization.
– Working knowledge of modern cloud-native architectures, including compute, storage, networking, data pipelines, and managed services.
– Strong background in cost analysis, forecasting, budgeting, and variance management.
– Comfort working directly with data, including writing SQL or using AI-assisted tools to query and validate datasets.
– Experience building dashboards and BI outputs that are accurate, intuitive, and useful for both engineers and leaders.
– Demonstrated ability to drive adoption and behavior change in day-to-day engineering workflows.
– Experience delivering cross-functional programs end to end, often without direct authority or a dedicated team.
– Familiarity with FinOps principles such as shared ownership, showback/chargeback, unit economics, and optimization strategies.
– Strong communication skills with the ability to tailor technical and financial concepts for engineering, finance, and executive audiences.

## Preferred Skills

– Familiarity with cloud cost management tools such as AWS Cost Explorer, GCP Billing, Azure Cost Management, CloudHealth, or Cloudability.
– Experience with data platforms and AI/ML workloads, including cost drivers for batch processing, streaming, storage, and model training/inference.
– Exposure to showback/chargeback models, cost allocation strategies, or product-level unit economics.
– Experience improving data models or pipelines used for analytics, reporting, or financial attribution.
– Familiarity with BI tools such as Mode, Tableau, or Looker, with a strong eye for dashboard usability and design.
– Background in a technical role such as engineering, TPM, infrastructure, data, or engineering operations before moving into a more cross-functional or business-oriented role.
– Experience in a high-growth or rapidly scaling environment.

## Cloud Platforms & Technologies

– **Cloud Cost Management Tools:** AWS Cost Explorer, GCP Billing, Azure Cost Management, CloudHealth, Cloudability
– **BI / Reporting Tools:** Mode, Tableau, Looker
– **Querying:** SQL
– **Workload and Infrastructure Areas:** cloud infrastructure, AI/ML, data workloads, SaaS tools, compute, storage, networking, data pipelines, managed services, batch processing, streaming, model training, model inference, API consumption

## FinOps Responsibilities

– Track engineering spend across cloud, AI/ML, data, and SaaS environments.
– Build forecasts and support budget planning with Finance and engineering leaders.
– Surface anomalies, trends, and optimization opportunities in spend.
– Embed cost considerations into roadmap and architecture discussions.
– Lead rightsizing, commitment, and workload-efficiency initiatives.
– Establish showback/chargeback, unit economics, and cost ownership models.
– Improve cost visibility, forecasting, and operational efficiency through tooling and automation.
– Encourage cost-aware behavior without slowing engineering delivery.

## Benefits

– Base pay range: $172.8K–$237.6K, with pay determined by scope, responsibilities, work experience, skillset, and location.
– Additional compensation may include equity and/or commission, depending on the role offered.
– Medical, dental, vision, and 401(k) coverage.

## Why You Might Be Interested

This role offers the chance to shape FinOps practices across cloud, AI/ML, data, and SaaS spending in a cross-functional environment. It will suit someone who can turn technical and financial data into decisions that engineers, Finance, and executives can act on. The work combines forecasting, reporting, optimization, and process design, with a clear emphasis on helping teams understand and own their costs.