Senior Manager, Credit

About this role

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 Engine recognition as TravelTech's Reservation Platform of the Year (2026), a spot on Inc.'s Power Partners list (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. About the Role Engine X is growing fast and credit decisioning is a core risk control.

The Sr. Manager, Credit owns the underwriting operating model for Engine’s corporate card: the credit standard, the backtest infrastructure, the portfolio analytics, and the team executing against it. The end state is a credit function where the majority of applications are decided automatically, with human judgment reserved for complexity and edge cases. You're the person who builds the bridge from here to there — owning the manual operation today while driving the criteria, model inputs, and policy decisions that make automation possible.

What You'll Do Hold the credit team accountable to the underwriting standard — set decision quality expectations, review portfolio performance, and ensure the team executes with consistency and rigor. Write and own the credit underwriting standard: what signals support which limits, what triggers exception treatment, and what the escalation path is. Build backtest infrastructure — run proposed rule changes against historical application data before deploying, including false positive analysis on approved accounts.

Partner with Data Science on credit model inputs, validation, and the transition from manual review to automated decisioning. Lead and develop the credit team — set expectations, provide feedback, and build analytical capability. Deliver portfolio analytics to leadership — delinquency rates, cohort performance, limit utilization, and exception trends. Coordinate with Fraud Operations on application fraud signals that overlap with underwriting decisions.