## About the Role

This FinOps role supports Plaid’s engineering-related spend across cloud infrastructure, AI/ML and data workloads, third-party SaaS tools, and other technical investments tied to product and internal platform delivery.

The team’s focus is to make spend visible, accountable, and easier to optimize without slowing engineering execution. You will work closely with Engineering, Product, Finance, TPMs, and engineering leaders to help teams understand cost drivers, make informed tradeoffs, and align investment decisions with business priorities.

The role is designed for someone who can turn technical spend data into clear forecasts, practical reporting, and actionable optimization work across a fast-moving, cloud-native environment.

## Key Responsibilities

– Track and analyze engineering spend across cloud, AI/ML, data platforms, and SaaS to surface trends, anomalies, and optimization opportunities.
– Build spend forecasts and partner with Finance and engineering leaders on assumptions, drivers, and risk factors.
– Work with Engineering, Product, and TPMs to factor cost into roadmaps, architecture choices, and execution plans.
– Lead optimization efforts such as rightsizing, commitment strategies, and workload efficiency improvements.
– Create dashboards and reporting that make spend understandable for both engineers and executives.
– Help establish cost ownership practices, including showback/chargeback and unit economics.
– Support tooling and automation efforts that improve visibility, forecasting, and operational efficiency.
– Encourage data-driven decision-making that balances cost, performance, reliability, and delivery speed.

## Required Skills

– 6–10+ years of relevant experience in engineering, infrastructure, data, or finance within a cloud-native or SaaS environment.
– Experience partnering closely with engineering teams on cloud infrastructure, data platforms, AI/ML workloads, or SaaS spend.
– Working understanding 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 using SQL, or AI-assisted tools for SQL, to explore data and validate assumptions.
– Experience building high-quality dashboards and BI outputs that are clear and easy to use.
– Proven ability to drive adoption and behavior change in engineering workflows.
– Experience delivering cross-functional programs end to end without direct authority or a dedicated team.
– Familiarity with FinOps practices such as shared ownership, showback/chargeback, unit economics, and optimization strategies.
– Strong communication skills with the ability to adapt technical and financial concepts for engineering, finance, and executive audiences.

## Preferred Skills

– Hands-on experience with cloud cost management tools such as AWS Cost Explorer, GCP Billing, Azure Cost Management, CloudHealth, Cloudability, or similar.
– Experience supporting 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, Looker, or similar.
– Prior experience in a technical role before moving into a more cross-functional or business-oriented position.
– Experience in a high-growth or rapidly scaling environment.

## Cloud Platforms & Technologies

### Cloud Providers
– AWS
– GCP
– Azure

### FinOps / Cost Management Tools
– AWS Cost Explorer
– GCP Billing
– Azure Cost Management
– CloudHealth
– Cloudability

### Programming / Query Languages
– SQL

### BI / Visualization Tools
– Mode
– Tableau
– Looker

### Workloads / Architecture Areas
– Cloud infrastructure
– AI/ML workloads
– Data platforms
– Data workloads
– SaaS tools
– Compute
– Storage
– Networking
– Data pipelines
– Managed services
– Batch processing
– Streaming
– Model training
– Inference

## FinOps Responsibilities

– Monitor engineering spend and identify opportunities to optimize costs.
– Build forecasts for technical spend and maintain assumptions, drivers, and risk visibility.
– Partner on cost-aware planning for roadmaps, architecture, and delivery execution.
– Lead optimization initiatives such as rightsizing, commitments, and workload efficiency.
– Develop reporting and dashboards that support both operational and executive decision-making.
– Implement shared ownership models, showback/chargeback, and unit economics practices.
– Improve tooling and automation to increase spend visibility and forecasting accuracy.
– Help teams make informed tradeoffs between cost, reliability, performance, and velocity.

## Benefits

– Medical, dental, and vision coverage.
– 401(k) plan.
– Equity and/or commission may be included, depending on the position offered.
– Compensation range: $172.8K – $237.6K.

## Why You Might Be Interested

This role offers broad exposure to FinOps across cloud, AI/ML, data, and SaaS spend in a product-driven environment. It combines forecasting, reporting, optimization, and cross-functional partnership with Engineering, Product, Finance, and TPMs. The scope is well suited to someone who wants to influence technical decisions through data and help establish stronger cost ownership practices.