FinOps Engineer

Sutherland

About The Position

Sutherland is seeking an experienced FinOps Engineer who will own cloud financial management across our GCP and AWS environments. Cost visibility, optimization, and stakeholder communication are your primary focus. As a secondary responsibility, you will contribute to CloudOps and SRE practices, giving you a complete view of the infrastructure you help optimize.

Requirements

7+ years of cloud infrastructure or engineering experience, with at least 3 years in a dedicated FinOps, cloud cost management, or platform economics role.
Hands-on GCP cost tooling experience — Billing export, Recommender, CUD analysis.
Proven track record of delivering measurable cloud cost savings.
Strong communication skills — you can translate a complex cost anomaly into a clear story for a non-technical stakeholder.
Experience building and maintaining tagging strategies and cost allocation frameworks.
Comfortable in a Kubernetes environment — you understand what node pools and resource limits mean in cost terms.

Nice To Haves

FinOps Foundation certification (FOCP) or equivalent.
Experience with CloudZero, Apptio Cloudability, or similar FinOps platforms.
SRE or CloudOps background — you understand what drives cloud spend from the inside.
AWS cost management experience: Savings Plans, Cost Anomaly Detection.

Responsibilities

Own cloud cost reporting across GCP and AWS — weekly, monthly, and ad hoc dashboards for engineering and business stakeholders.
Drive cost optimisation: rightsizing, committed use discounts (CUDs / RIs), idle resource cleanup, storage lifecycle policies, and network egress reduction.
Design and enforce a cloud tagging strategy — define taxonomy, automate compliance checks, and resolve untagged resources.
Build showback and chargeback models so teams understand and are accountable for their spend.
Lead monthly FinOps review meetings — present spend trends, highlight anomalies, explain variances, and recommend actions.
Set up and maintain budget alerts, anomaly detection, and forecasting models.
Partner with product and engineering to embed cost awareness early in architecture and feature design.
Support day-to-day cloud operations on GCP and AWS: incident response, monitoring hygiene, and infrastructure health reviews.
Contribute to Datadog observability — dashboards, monitors, and alert tuning, particularly for cost-correlated signals.
Participate in on-call rotations and post-incident reviews with a cost lens on RCA.
Work with platform and SRE teams on Kubernetes cluster efficiency — node utilisation, autoscaling, and namespace-level cost attribution.