$ cat jobs/staff-software-engineer-cloudbeds-4a871866cedb.json
Staff Software Engineer
What Makes Cloudbeds Unique At Cloudbeds, we're not just building software, we’re transforming hospitality. Our intelligently designed platform powers properties across 150 countries, processing billions in bookings annually. From independent properties to hotel groups, we help hoteliers transform operations and uplevel their commercial strategy through a unified platform that integrates with hundreds of partners. And we do it with a completely remote team. Imagine working alongside global innovators to build AI-powered solutions that solve hoteliers' biggest challenges. Since our founding in 2012, we've become the World's Best Hotel PMS Solutions Provider and landed on Deloitte's Technology Fast 500 again in 2024, but we're just getting started. Location: Remote (Europe) How You'll Make an Impact: As a Staff Software Engineer in Revenue Intelligence, you will shape the technical direction of systems that help hospitality businesses understand demand, identify revenue opportunities, and make better pricing and commercial decisions. You will work across customer-facing applications, backend services, data pipelines, analytical stores, recommendation workflows, and cloud infrastructure. This is a hands-on leadership role: you will solve complex engineering problems directly while establishing architecture, improving reliability and security, reducing systemic technical debt, and aligning multiple teams around durable technical decisions. Your impact will extend beyond individual features to the quality, scalability, operability, and long-term evolution of the Revenue Intelligence platform. Our Revenue Intelligence Team: The Revenue Intelligence team builds products and platforms that turn hospitality data into practical decisions for hoteliers, including pricing recommendations, demand insights, reporting, automation, and commercial opportunities. Our systems span frontend experiences, APIs, distributed services, data processing, machine-learning workflows, and produc