This is a remote position.

Location: Currently remote but may transition to onsite in Future

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.