Senior Financial Analyst

A2Z Sync•Denver, CO

10d•$83,000 - $113,000•Hybrid

About The Position

A2Z Sync is building the operating system for U.S. automotive retail — a market measured in trillions, still operating on disconnected, decades-old systems. We replace that friction with the velocity of a single platform, integrating digital insights with in-store operations to deliver transparent transactions for car buyers and increased profitability for dealers. We are a Series B, AI-native SaaS company experiencing tremendous growth. We are transitioning from a traditional SaaS company into an AI-native one — and we are doing it from Denver with a small, sharp team that ships fast and plays hard. This role sits in our Denver office and requires 3 days per week in-office. Why This Role Exists Our finance function is small, fast, and forever evolving forward. The infrastructure is in place — automated reporting, a governed data layer, department P&Ls that publish the day the books close. What is evolving is the layer on top: the revenue and go-to-market analytics, the KPI framework leadership runs on, and the insight that turns all of it into decisions. That is what this seat is for. This is a green-field role. You will define how we measure the business — the metrics that go in front of leadership, the unit economics behind our go-to-market motion, and the analysis that shapes pricing, hiring, and investment decisions. You will help evolve and build from the ground up. You will work directly with the Director of Finance, partner across sales and revenue operations, and have visibility with company leadership from your first month. If you want a role where the models are already built and the questions are already framed, this is not it.

Requirements

Bachelor's degree in finance, accounting, economics, or a related field.
3–5 years of progressive FP&A, corporate finance, or strategic finance experience, including direct ownership of a forecast.
Experience in a SaaS, subscription, or recurring-revenue business, with a strong command of ARR/MRR, bookings, NRR, churn, gross margin, CAC, LTV, payback period, and pipeline coverage.
Experience partnering cross-functionally with sales, marketing, or revenue operations.
Advanced Excel / Google Sheets — driver-based modeling, scenario analysis, and large data manipulation (pivot tables, XLOOKUP / INDEX-MATCH).
Demonstrated ability to build a model someone else can pick up — versioned, documented, and auditable.
Strong written communication: you can explain a variance in two sentences to someone who does not work in finance.
Experience using automation in finance workflows, along with curiosity about applying AI tools such as Claude, Gemini, or similar technologies to analysis and process improvement.
Comfort building in white space — you can take an undefined problem and return with a framework.
Strong organizational skills and the ability to juggle competing priorities without dropping balls.
Able to work 3 days per week on-site in Denver.

Nice To Haves

Experience automating a recurring report, model, or reporting process.
Prior experience at a high-growth startup, technology company, or SaaS environment.
Background in automotive retail, dealer technology, or another vertical SaaS with usage- and implementation-driven revenue.
Experience supporting board reporting, 409A valuations, or fundraise diligence.
Experience with our stack or similar tools: Rillet, Ramp, HubSpot, BambooHR, Carta.

Responsibilities

Build and maintain the AI-native revenue model with new customer ramp, go-live timing, expansion, contraction, and churn.
Partner with revenue operations to ensure the go-to-market muscle is driving accurate revenue targets and metrics.
Model the economics of the go-to-market motion — sales and marketing investment, pipeline conversion, and the return on each channel.
Build the monthly plan-to-forecast-to-actual bridge, with every delta explained by a driver rather than a plug.
Prepare the analysis and flag anomalies ahead of revenue reviews, so the conversation starts from validated numbers.
Define and maintain the company metric set — ARR/MRR, NRR, churn, CAC, LTV, payback period, and pipeline coverage — with definitions everyone works from.
Put those metrics in front of leadership on a regular cadence, with the interpretation attached, not just the number.
Track performance against SaaS peer benchmarks so leadership knows where we stand, not only where we are.
Bring the analysis nobody asked for when the data warrants it — the trend that changes a decision before it shows up in the P&L.
Build and maintain the driver-based models behind the plan — subscription and recurring revenue, integration costs, and hosting. Keep them versioned, documented, and auditable.
Drive the quarterly reforecast to completion on time, with an immutable baseline and a clear record of what changed and why.
Stress-test the plan against pricing, ramp, churn, and cost assumptions so leadership can see the range, not just the point estimate.
Model timing, vacancy impact, and fully loaded cost into the plan.
Own the analysis behind third-party integration and data costs — a core cost-of-revenue driver of the business. Monitor variance by vendor and flag drift before it compounds.
Monitor cloud infrastructure spend with in-month early reads, not post-close surprises.
Track software, vendor, and contractor spend against budget; produce department-level opex variance with commentary that explains cause rather than restating magnitude.
Maintain gross margin by revenue stream, along with unit economics and cost-to-serve models.
Deliver company and department-level BVA every close, with review time reserved before distribution.
Draft the monthly narrative — what moved, why, and what it means for the rest of the year.
Maintain and extend the executive and department dashboard suite so it stays self-serve and always current.
Prepare the recurring financial schedules and analysis that feed the board package.
Partner with accounting on department-level actuals and classification so every number ties across the GL, the platform, and every report that cites it.
Run validation and quality checks across revenue, cost of revenue, and opex before anything leaves finance.
Find and automate repetitive tasks to redeploy time to analysis.
Document the models and the logic you own as you build them, so the function is never keyed to one person.