[Remote] Finance Engineer

Note: The job is a remote job and is open to candidates in USA. AssemblyAI builds Voice AI models that power voice applications and large-scale audio processing. The Finance Engineer will build data pipelines, dashboards, automation, internal tools, and agent workflows that support finance and revenue operations, including unit economics, technology budgeting, deal desk, and sales compensation. The role partners with finance, sales, and engineering leadership to create trusted financial data and operational systems.

Responsibilities

• Own unit economics — build and maintain cost-to-serve, gross margin, and contribution models by product, customer, and workload, rebuilt from source data rather than spreadsheets
• Own budgeting for the technology organization: cloud and GPU/TPU spend attribution, vendor and capacity forecasting, and the models engineering leadership uses to plan
• Build and run finance dashboarding — a single source of truth for burn, runway, margin, and company KPIs that the leadership team and board can trust
• Automate budget-vs-actuals: live department-level views and overspend alerts that make budget and margin targets enforceable without manual chasing
• Build the deal desk framework: pricing and discount guardrails derived from the margin model, automated quote and order-form generation, and routing so only non-standard deals need escalation — raising deal throughput without finance in the loop on every deal
• Automate sales compensation: implement a comp tool and the pipelines behind it so statements, accelerators, and disputes run from CRM and billing data instead of spreadsheets
• Design and ship finance agents and internal tools — workflows that pull, reconcile, and summarize financial data so non-engineers get answers without filing a ticket
• Build and maintain the finance data layer: pipelines from billing, payroll, cloud, and accounting systems into a well-modeled warehouse
• Partner with the VP Finance, Controller, and engineering leadership to trace discrepancies to their real source and ship fixes that hold

Skills

• Strong data engineering fundamentals, including SQL, Python, warehouse modeling, and orchestration (dbt, Airflow, or equivalent)
• Measurement discipline. You define what a number is for before you build it, you stay skeptical of your own results until they reconcile, and you treat an unexplained variance as a problem rather than noise
• Appetite for the whole stack. Your core strength might be pipelines, but when a margin number looks wrong and traces back to a billing event or a cloud tag, you want to go find it yourself. The people who do well here went deep in one area first, then kept expanding outward
• Experience working with financial or operational data — revenue, cost, billing, payroll, or cloud spend — and enough fluency in revenue, cost, and margin structures to model them without hand-holding
• A track record of building internal tools, dashboards, or automations that non-engineers rely on daily
• Familiarity with LLM-based agent workflows and comfort applying them to real operational processes, with a clear-eyed view of where they break
• Enthusiasm for automating existing processes — you thrive on making manual work disappear and making systems faster and more reliable
• Strong Python skills; experience with accounting, billing, or ERP system APIs (NetSuite, Stripe, or similar) is a plus
• Excellent communication and a collaborative mindset — you can explain what a number means, and what it doesn't, to a VP Finance, a Controller, and an engineering lead
• Experience with accounting, billing, or ERP system APIs (NetSuite, Stripe, or similar) is a plus
• * Revenue operations exposure: experience with CRM or CPQ data (Salesforce, HubSpot, or similar), sales comp tools (CaptivateIQ, Spiff, QuotaPath, or similar), or running a deal desk

Company Overview

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