---
title: 'Senior FP&A Analyst - Data Center Finance at Lambda'
canonical: 'https://feeny.ai/job/senior-fp-a-analyst-data-center-finance-lambda-san-francisco-jjbzx2ddf5zk'
type: 'job'
last_seen: '2026-11-08'
---

# Senior FP&A Analyst - Data Center Finance at Lambda

- **Company:** Lambda
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-11-04
- **Last confirmed live:** 2026-11-08
- **Apply:** https://jobs.ashbyhq.com/Lambda/c9b27265-ffb4-482a-b778-2c572796b325

## Job description

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU.

If you'd like to build the world's best AI cloud, join us.

*Note: This position requires presence in our San Francisco, San Jose, or Bellevue office location 4 days per week; Lambda’s designated work from home day is currently Tuesday.

## About the Role

As a member of the DC / Infrastructure Finance team, you will report to the Manager of DC / Infrastructure Finance and partner with Data Center Development, Data Center Operations, and Program Operations. You will maintain visibility into site costs, cash requirements, and investment performance from development through deployment and operations, helping leaders manage budgets, evaluate tradeoffs, and deliver capacity efficiently.

## What You'll Do

Data Center Development Budgets

- Build and maintain project budgets for data center construction, site improvements, power and cooling infrastructure, and commissioning in partnership with project owners
- Track actual costs, contractor commitments, change orders, and remaining scope against approved budgets; maintain estimates to complete and forecast total project costs
- Evaluate contingency usage and changes in scope or schedule, flag potential overruns, and recommend corrective actions

Deployment Timing and Cash Forecasting

- Translate construction milestones, contractor payment schedules, and commissioning plans into project cash forecasts
- Model the financial impact of delays, phased deployments, and changes in capacity readiness; coordinate equipment assumptions with Supply Chain Finance
- Own site and construction payment forecasts as inputs to the consolidated cash forecast for Corporate FP&A and Treasury, clearly separating changes in total cost from changes in payment timing

Data Center Operating Costs

- Own site operating cost forecasts and budget versus actual analysis across power, colocation, rent, maintenance, and other infrastructure costs
- Build driver-based models using contracted capacity, energized capacity, deployed GPUs, power consumption, and operating assumptions
- Explain cost and margin variances caused by deployment ramps, power rates, capacity utilization, and contract terms; identify opportunities to improve efficiency

Investment Economics and Performance

- Support financial evaluation of new sites, expansions, and infrastructure commitments, including project returns, payback, and sensitivity analysis
- Analyze capital cost per MW, operating cost per GPU hour, and other site economics using consistent cost definitions and assumptions
- Own site-level models for investment committee reviews and post-investment assessments, incorporating equipment cost inputs from Supply Chain Finance and comparing project costs, deployment timing, and operating performance with approved investment assumptions

Planning and Accounting Partnership

- Develop site and project inputs for the annual operating plan, quarterly forecasts, and long-range infrastructure plan
- Partner with Accounting on accrual inputs, cost coding, and lease accounting (ASC 842) inputs to improve the accuracy of project and operating cost reporting
- Own facility in-service timing, from construction in progress to placed in service, as an input to the infrastructure depreciation forecast
- Work with Supply Chain Finance to reconcile site infrastructure and equipment budgets, preventing gaps or double counting in consolidated deployment costs

Reporting and Process Improvement

- Prepare clear project and site reporting for business reviews, executive discussions, and Board materials, highlighting financial risks and decisions needed
- Connect project schedules, commitments, and financial actuals using the team’s shared commitments data (purchase orders, invoices, and payments) in repeatable reporting and planning tools
- Build and maintain repeatable models and workflows in Pigment and AI-assisted tools, reducing manual spreadsheet work
- Improve the quality and consistency of infrastructure data, forecasts, and reporting as Lambda scales its data center portfolio

You

- Have a Bachelor’s degree in Finance, Accounting, Economics, or a related field
- Have 4+ years of experience in FP&A, project finance, infrastructure finance, or a related analytical role
- Have experience managing project budgets, capital spending forecasts, or operating cost models in data centers, construction, energy, telecom, or another capital-intensive business
- Understand how project commitments, actual costs, estimates to complete, and payment timing translate into financial forecasts
- Possess strong financial modeling and Excel skills, including the ability to build driver-based models and evaluate investment returns and scenarios
- Are comfortable working in enterprise planning systems and building repeatable, automated workflows rather than one-off spreadsheets
- Can reconcile financial and operational data, identify the causes of variances, and turn findings into practical recommendations
- Communicate clearly with finance, engineering, construction, and operations stakeholders
- Are comfortable working independently in an ambiguous, fast-paced environment while managing multiple projects and deadlines

## Nice to Have

- Experience supporting data center development, construction, or commissioning, including estimate-to-complete and contingency tracking
- Familiarity with colocation and lease contracts, ASC 842 lease accounting, or power procurement and PPAs
- Exposure to project finance, SPV structures, or powered land development
- Familiarity with GPU infrastructure, site capacity metrics (MW, PUE), or cloud infrastructure unit economics
- Working proficiency in SQL or BI tools, and experience with Pigment, NetSuite, or Coupa
- Prior experience in a startup or high-growth organization

Salary Range Information The annual salary range for this position has been set based on market data and other factors. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.

## About Lambda

- Founded in 2012, with 500+ employees, and growing fast
- Our investors notably include TWG Global, US Innovative Technology Fund (USIT), Andra Capital, SGW, Andrej Karpathy, ARK Invest, Fincadia Advisors, G Squared, In-Q-Tel (IQT), KHK & Partners, NVIDIA, Pegatron, Supermicro, Wistron, Wiwynn, Gradient Ventures, Mercato Partners, SVB, 1517, and Crescent Cove
- We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG
- Our values are publicly available: https://lambda.ai/careers
- We offer generous cash & equity compensation
- Health, dental, and vision coverage for you and your dependents
- Wellness and commuter stipends for select roles
- 401k Plan with 2% company match (USA employees)
- Flexible paid time off plan that we all actually use

## Equal Opportunity Employer

Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.

## About Lambda

## Company Overview
- **One-liner**: Lambda builds supercomputers and cloud infrastructure for training and deploying large-scale AI models, from single GPUs to gigawatt-scale AI factories.
- **Entity Type**: Private (funding stage not publicly disclosed; founded by ML engineers)
- **Headquarters**: San Francisco, California, USA
- **Founded**: 2012
- **Founders**: Stephen Balaban and Michael Balaban

## Core Business
- Primary industry: AI infrastructure / cloud computing for machine learning.
- Target customers: Frontier AI labs building large foundation models, hyperscalers scaling global AI infrastructure, and enterprises deploying AI in regulated industries (B2B, Enterprise).
- Mission: “Make compute as ubiquitous as electricity and give everyone in America the power of superintelligence” (also “One person, one GPU”).

## Products & Services
- **The Superintelligence Cloud**: A suite of cloud computing offerings specifically built for AI workloads, including:
  - **GPU Instances**: On-demand NVIDIA HGX B200, H100, and GB300 NVL72 instances for prototyping and testing.
  - **Managed Clusters**: Dedicated, single-tenant clusters (e.g., NVIDIA GB300 NVL72, HGX B200/H100) with full management and co-engineering from Lambda’s team.
  - **1-Click Clusters™**: Rapidly deployable clusters for training and inference.
  - **Superclusters**: Large-scale AI factories integrating high-density power, liquid cooling, and high-bandwidth interconnects.
- **AI Infrastructure Hardware**: Modular AI factory designs and NVIDIA-based systems for on-premise or colocation deployment.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metric**: Total funding not publicly available; revenue not disclosed.
- **Notable Customers/Partners**: “World’s most advanced AI organizations” (frontier labs, hyperscalers, regulated enterprises). Leadership includes former executives from cloud and networking companies.
- **Growth Signals**: Active hiring across engineering, storage, security, and procurement roles; building AI factories at gigawatt scale; SOC 2 Type II certified; expanding from San Francisco to San Jose, CA.

## Competitive Advantages
- **AI‑First DNA**: 100% of engineering, operations, and support dedicated to AI – founded by ML engineers in 2012.
- **Single‑Tenant Isolation**: Shared‑nothing architecture for security and performance, with hardware‑level isolation.
- **Full‑Stack Expertise**: Co‑engineering from the same team building the infrastructure, enabling deep optimization for large training runs.
- **Hacker Culture**: Rooted in the Noisebridge hackerspace values of do‑ocracy, low ego, and “be excellent to each other.”
- **Performance**: Rack‑scale NVIDIA systems (GB300, B200, H100) with high‑speed interconnects (NVIDIA Quantum‑2 InfiniBand).

## Strategic Focus
- Scaling infrastructure to support the next generation of superintelligence, including gigawatt‑scale AI factories.
- Enabling frontier labs to train trillion‑parameter models and serve billions of tokens in production.
- Expanding compliance and security capabilities for regulated industries.
- Growing the cloud platform (The Superintelligence Cloud) as the primary go‑to‑market offering.

## Why Work Here
- **Culture**: Hacker ethos (Noisebridge roots), low ego, no yelling, no politics, no crypto. Values: build, move fast, care, be excellent to each other.
- **Work Environment**: Fast‑paced, high‑change, outcome‑focused. Emphasis on technical excellence and curiosity. Anonymous feedback encouraged.
- **Interview Process**: Clear, structured steps (recruiter chat → hiring manager → technical assessment → panel interviews → reference/offer). Pedigree is not everything; focus on what you’ve built.
- **Location & Remote**: Offices in San Francisco and San Jose, CA. FAQ page addresses remote/hybrid policy (details not provided in available snippets); some roles appear on‑site.
- **Perks**: Benefits, time off, and other perks are listed on the careers site (specifics not extracted here).

## Sources
1. [lambda.ai/about](https://lambda.ai/about)
2. [lambda.ai/](https://lambda.ai/)
3. [lambda.ai/careers](https://lambda.ai/careers)
4. [lambda.ai/leadership](https://lambda.ai/leadership)

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