The FinOps function is responsible for financial accountability, visibility, and optimization across all engineering-related spend at Plaid. This includes cloud infrastructure, AI/ML and data workloads, third-party SaaS tools, and other technical investments that support Plaid's products and internal platforms.
The team operates at the intersection of Engineering, Product, and Finance, ensuring that spending decisions are transparent, intentional, and aligned with product strategy and business priorities. Rather than functioning as a cost-control or approval layer, FinOps enables teams to understand, own, and optimize their spend while maintaining engineering velocity.
Responsibilities- Monitors and analyzes engineering spend across cloud, AI/ML, data platforms, and SaaS, identifying trends, anomalies, and optimization opportunities.
- Builds and maintains forecasts for engineering spend, partnering with Finance and engineering leaders to understand drivers, assumptions, and risks.
- Partners with engineering, product, and TPMs to incorporate cost considerations into roadmaps, architectural decisions, and execution plans.
- Leads cost optimization initiatives, such as rightsizing, commitment strategies, and workload efficiency improvements, in collaboration with engineering owners.
- Creates and maintains dashboards and reporting that make spend understandable and actionable for both engineers and executives.
- Implements FinOps practices and processes, including showback/chargeback models, unit economics, and cost ownership frameworks.
- Partners on tooling and automation, working with data and engineering teams to improve cost visibility, forecasting accuracy, and operational efficiency.
- Drives alignment and behavior change, helping teams balance cost, performance, reliability, and velocity through data-driven decision making.
Requirements- 6-10+ years of relevant experience working at the intersection of engineering, infrastructure, data, or finance in a cloud-native or SaaS environment.
- Proven experience partnering closely with engineering teams to influence decisions involving cloud infrastructure, data platforms, AI/ML workloads, or SaaS spend.
- Working understanding of modern cloud-native architectures, including core components such as compute, storage, networking, data pipelines, and managed services-enough to engage credibly with engineers on design, tradeoffs, and cost drivers.
- Strong foundation in cost analysis, forecasting, budgeting, and variance management, with the ability to translate data into clear, actionable insights.
- Comfort working directly with data, including writing SQL (or effectively using AI-assisted tools to do so) to explore datasets, validate assumptions, and answer ad hoc questions.
- Experience building clear, high-quality dashboards and BI artifacts that are not only accurate, but intuitive and delightful for engineers and leaders to use.
- Demonstrated success driving adoption and behavior change-embedding cost awareness into day-to-day engineering workflows, not just producing reports.
- Experience owning and delivering cross-functional programs end-to-end, often without direct authority or a dedicated team.
- Familiarity with FinOps principles and practices (e.g., shared ownership, showback/chargeback, unit economics, optimization strategies).
- Strong communication skills, with the ability to tailor complex technical and financial concepts for engineering, finance, and executive audiences.
Nice to Haves- Hands-on familiarity with cloud cost management tools (e.g., AWS Cost Explorer, GCP Billing, Azure Cost Management, CloudHealth, Cloudability, or similar).
- Experience working with or supporting data platforms and AI/ML workloads, including understanding 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 that support analytics, reporting, or financial attribution.
- Familiarity with BI tools such as Mode, Tableau, Looker, or similar-and a strong eye for dashboard usability and design.
- Background in a technical role (e.g., engineering, TPM, infra, data, or engineering operations) before moving into a more cross-functional or business-oriented position.
- Experience operating in a high-growth or rapidly scaling environment, where cost structures and investment priorities are evolving quickly.
