Introduction to the team

Commerce Data Engineering provides the source of truth for Expedia Group’s booking transactions. Our team delivers accurate, comprehensive data that powers insights and decision making across the company.

We capture the full booking lifecycle from the moment a traveler clicks “book,” enabling visibility into booking value, profitability, and other essential commercial metrics. As we grow, we are expanding our platform with richer contextual signals that strengthen insight quality and open new possibilities for personalization and automation.

By advancing the depth and intelligence of commerce data, we help enable the next generation of agentic experiences at Expedia Group. These capabilities allow systems and teams to reason, act, and deliver more proactive and effective outcomes for travelers and partners.

In this role, you will:

• Design, develop, and maintain distributed backend services and APIs with a focus on reliability, scalability, and performance across multiple product or domain areas

• Translate functional and nonfunctional requirements into robust low level designs, including API specifications, data models, and integration patterns that other engineers can successfully implement

• Implement clean, well tested code using modern engineering practices, perform code reviews for peers, and drive continual improvements to code quality, observability, and operational readiness

• Own the full service lifecycle for the components you build, including deployment, monitoring, incident response, and iterative improvements based on production telemetry and data

• Collaborate with product, architecture, and cross functional engineering teams to deliver solutions that are secure, compliant, and reusable across multiple domains and platforms

• Safely integrate and operate AI/ML enabled solutions that improve outcomes, including applying AI driven systems, tools, or workflows in your development process and services

Minimum Qualifications:

• Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience

• 5+ years of relevant professional experience

• Professional software engineering experience designing, building, and operating backend or full stack services, typically including several years working with distributed systems or service-oriented architectures

• Proven ownership of one or more services or components in production, including responsibility for design, implementation, quality, and ongoing operations

• Proficiency in at least one modern programming language, strong understanding of data structures and algorithms, and experience with API design, low level system design, and data modeling

• Familiarity with AI driven systems, tools, or workflows and applying AI/ML concepts to real world products

Preferred Qualifications:

• Experience designing and evolving large scale, high throughput services and data models that support multiple products or domains while maintaining performance and reliability

• Demonstrated strength in system design (including low level designs), API contract definition, and schema design, with a track record of guiding others toward robust architectural patterns

• Track record of driving operational excellence for owned services, including deep use of logging, metrics, and alerting, and leading incident response, root cause analysis, and follow through on remediation

• Experience integrating and safely operating AI/ML enabled features or tooling in production services (for example, using model driven recommendations, intelligent routing, or AI assisted development workflows), including strong proficiency using AI tools as part of everyday software engineering, with sound judgment about when human‑only review is required

• Experience designing or contributing to frameworks, tooling, or workflows that enable AI‑assisted development at scale (for example, agent‑based coding systems, model‑context protocols, or spec‑driven development methodologies), with attention to reliability, safety, and developer productivity

• Experience defining, applying, or influencing AI governance practices to ensure responsible, secure, and compliant use of AI systems, including considerations around data privacy, model behavior, auditability, and operational risk

• Actively applies data‑driven decision making by using experimentation, telemetry, and analytics to refine service behavior and technical design, and leverages foundational AI/ML knowledge to influence service, architecture, and tooling direction