Cloud Cost Governance Lead - Multi-Cloud FinOps

Job Description This is a remote position. Location: Currently remote but may transition to onsite in Future That is a keyperson and governance risk , and today we run labs and sandboxes across Azure, AWS, and GCP . This is a control-tower role, not a reporting role. We are not hiring another analyst who produces dashboards after the money is gone. Competency Weighting Cloud Weight Depth required Microsoft Azure 50–60% Primary platform. Must be the candidate's deepest, demonstrable area - years of hands-on billing/FinOps ownership, not certification-level familiarity. AWS 20–30% Strong working command. Must have personally owned AWS cost governance at scale, not just "supported" it. Google Cloud (GCP) ~10% Functional proficiency. Able to read GCP billing, run cost queries, and extend governance to GCP workloads independently. Core Accountability Own end-to-end cost governance, intelligence, and risk control across all CloudLabs Azure, AWS, and GCP environments. Single throat to choke for: no surprise spikes, real-time visibility, anomaly detection, root-cause closure, and refund/credit recovery. Accountable directly to the COO. Key Responsibilities 1. Multi-cloud cost ownership (primary) Own total cloud spends across Azure (lead), AWS, and GCP -every tenant, account, project, subscription, and ODL. Maintain a single source of truth : daily/weekly/monthly spend, with every dollar classified as Expected (revenue-linked), Internal (non-billable), or Unexpected (leak/anomaly). Be able to explain any line item — across any of the three clouds — on demand, without convening a war room. 2. Real-time monitoring & anomaly detection Azure (50–60%): Azure Cost Management + Partner Center, threshold/budget alerts per tenant, per deployment, per lab; native anomaly detection. AWS (20–30%): Cost Explorer, Cost & Usage Reports (CUR) , AWS Budgets, Cost Anomaly Detection, Organizations consolidated billing. GCP (~10%): Billing export to BigQuery , budget alerts, billing-account-level monitoring. Hard SLO: no anomaly on any cloud goes undetected beyond 24 hours. 3. Incident ownership & RCA Own every cost incident end-to-end across clouds - root cause, quantified impact, and the engineering fix. Maintain an RCA repository with recurring patterns (e.g., billing-after-deletion, orphaned resources, marketplace/AI-unit leakage) and preventive controls. 4. Platform ↔ cloud cost integrity Drive engineering to close lifecycle gaps where CloudLabs "deleted" status diverges from actual cloud state — on all three providers . Institute reconciliation checks and audit logs so deletion-to-billing integrity is verifiable, not assumed. 5. Data & pipeline ownership Own all cost data pipelines - Partner Center, AWS CUR, GCP BigQuery export, Power BI/SQL reporting layers. Guarantee data completeness (no repeat of the missing-May-usage gap) and schema resilience (no repeat of the Tier2MPNID pipeline break). 6. Refund, credit & recovery Identify all refund/credit-eligible scenarios across Azure (MS support escalations), AWS, and GCP; drive tickets to closure; maintain a recovery tracker with status and ownership. 7. Forecasting & optimization Build spend-prediction models tied to deployments, labs, and usage patterns across clouds. Drive commitment-based savings (Azure Reservations, AWS Savings Plans/RIs , GCP CUDs ), rightsizing, and budget controls. Requirements Must have (will be evidence-tested in interview): 8–12+ years in cloud/FinOps, with 5+ years personally owning Azure cost governance at meaningful scale (multi-tenant / CSP / EA / MCA). Must walk us through a real Azure overrun they detected, root-caused, and recovered — with numbers. 3+ years hands-on AWS cost ownership — must have built or run CUR-based reporting, Organizations billing, and Savings Plan/RI strategy themselves. Working GCP billing proficiency — must have independently queried billing export in BigQuery and stood up budget controls. Demonstrated ownership of cost anomaly detection that caught a real leak before it escalated — show us the before/after. Built (not just used) cost dashboards and data pipelines — Power BI / SQL / BigQuery — and kept them resilient through schema changes. Track record of driving engineering and finance to fix root causes , with examples of controls they put in place that stopped a problem from recurring.