Data Analyst

About Engine

Engine is the AI-native platform for intelligent travel. For a decade, Engine has built the proprietary technology and supply behind modern business travel, serving over 40,000 customers and nearly 2 million travelers, along with leading travel and AI companies that build on Engine's infrastructure. One system powers business travel, group travel, human agents, and AI agents.

That work has earned recognition as one of Fast Company's Best Workplaces for Innovators (2025), TravelTech's Reservation Platform of the Year (2026), and one of Built In's Best Places to Work (2020–2026).

We're looking for bold, ambitious people to help redefine how businesses manage and experience travel. Working here, you can expect exponential growth, an ambitious pace, full ownership, and high-caliber colleagues who push you to do the best work of your career. Every hire raises the bar.

Come outpace the ordinary and build the future of travel with us.

Your Mission

We’re looking for a Senior Data Analyst to build and own the data and analytics behind Engine’s internal finance function. In this role you’ll partner directly with our Head of Finance. You’ll use data, AI and automation to change how finance gets its insights: faster, more accurate and less manual. You’ll build the models, pipelines and analyses that leadership, the board and investors rely on.

What You’ll Do

• Partner with our Head of Finance as the lead data analyst for the finance team, scoping analytical needs and turning open-ended questions into rigorous, data-driven answers.
• Design and build the finance data layer in Snowflake, including clean, well-documented data models and pipelines that bring together ERP, billing, payroll and operational data into one trusted source of truth.
• Use AI and automation to remove manual finance work. That includes automated reconciliations, anomaly detection, self-serve reporting and AI-assisted analysis that speeds up month-end and planning cycles.
• Build and manage finance’s BI dashboards in Looker, giving leadership real-time visibility into revenue, margin, operating expenses and cash.
• Use advanced SQL to explore large, complex datasets and find what’s driving revenue, margin and cost performance.
• Apply statistical methods (e.g., regression, time-series forecasting, driver analysis) to improve forecast accuracy and support budgeting and long-range planning.
• Go beyond surface-level numbers to explain the “why” behind financial trends, quantify opportunities and deliver clear recommendations.
• Analyze operating expenses, headcount and vendor spend to find efficiencies and protect profitability as we scale.