Finops Architect

Role : Finops Architect Location : Santa Clara CA or Austin TX

The full job description covers all associated skills, previous experience, and any qualifications that applicants are expected to have.

• Onsite Responsibilities : You will design and build the foundational FinOps architecture for a large-scale Data Platform — the systems that enable cost visibility, efficiency, and accountability across thousands of workloads.

In this role, you will : Lead the design of a scalable cost attribution and chargeback framework spanning compute, storage, caching, GPU, and network layers.

Build instrumentation and data pipelines that collect usage telemetry, normalize cost data, and surface insights across both internal and cloud environments.

Partner with platform and infrastructure teams to integrate cost efficiency automation — including autoscaling, throttling, resource reclamation, and policy enforcement.

Develop dashboards and reporting systems that provide real-time visibility into utilization, waste, and savings opportunities.

Guide efficiency initiatives across teams by identifying underutilized clusters, GPU idle time, and unoptimized workloads to drive measurable cost reduction.

Collaborate with Finance, Capacity Planning, and Engineering to define budgeting models, KPIs, and showback / chargeback policies.

Mentor engineers across the organization in FinOps best practices and foster a culture of fiscal accountability within engineering.

This is a high-impact, cross-functional role.

You’ll work across cloud, AIML, and platform teams to ensure every dollar of compute delivers maximum value.

Minimum Qualifications 8+ years of experience in software, infrastructure, or platform engineering with a focus on performance, scalability, or cost optimization.

Deep technical understanding of cloud and on-prem compute, storage, GPU / accelerator, and network cost models.

Proven experience building cost attribution, chargeback, or FinOps automation systems at scale.

Strong programming proficiency in Python, Go, or Java, and familiarity with large data systems (Spark, Kubernetes, or distributed schedulers).

Hands-on experience with observability platforms, telemetry collection, and usage analytics (e.g., Prometheus, Grafana, Datadog, or custom metrics pipelines).

Ability to model resource usage, design allocation frameworks, and build automation that enforces efficiency policies.

Strong collaboration and communication skills to influence partner engineering, finance, and capacity planning teams.

Comfort with ambiguity and proven ability to define structure and frameworks in new domains.

A passion for driving measurable cost and efficiency improvements through engineering excellence.

Preferred Qualifications Experience in ML systems, GPU scheduling, or large-scale training / inference platforms.

Familiarity with AWS, GCP, or internal infrastructure cost constructs.

Background in capacity planning, forecasting, or resource optimization for shared infrastructure.

Experience designing data models, metrics, and pipelines for cost telemetry.

Contributions to open-source FinOps or observability frameworks.

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (or equivalent experience).