Software Development Engineer II

We are the Analytics Products team. We build Expedia Group's analytics platform and the products on it that help people across the business make decisions with trusted data. We are rebuilding it as an AI-native platform, adding conversational analytics, agents, and self-service products alongside the dashboards people use today. We use AI tools and agents every day to design, code, and ship, and we work in short cycles: build something, put it in front of users, and turn what works into capabilities others can reuse.

This role combines software development and analytics. You will build products on certified metrics, data, and AI, and work closely with the people who use them. It could suit an engineer who thinks like an analyst, or an analyst who has become a capable developer. Either way, we want someone who gets things done and cares about what to build as much as how to build it.

In this role, you will:

• Design, develop, test, and maintain reliable software solutions for analytics products, contributing across the full software development lifecycle.
• Apply system design, low-level design, API design, and data modelling practices to build scalable, maintainable services and product capabilities that both people and AI agents can use.
• Collaborate with engineers, product managers, analysts, and other stakeholders to translate business and analytical requirements into effective technical solutions, starting from the decisions users need to make.
• Diagnose complex technical issues, improve application performance and reliability, and contribute to operational excellence through monitoring, testing, and continuous improvement.
• Develop technical aptitude across relevant domains, applying transferable engineering practices to backend services, data-intensive workflows, and AI-powered analytics products.
• Safely integrate and operate AI/ML-enabled solutions that improve outcomes, using certified metrics and checking outputs for accuracy, while applying familiarity with AI-driven systems, tools, or workflows and AI/ML concepts to real-world products.
• Build AI-powered analytics features such as conversational analytics, natural-language-to-SQL, automated insight summaries, and self-service decision tools.
• Test new ideas quickly and ship early, then turn what works into reusable, self-service capabilities others can build on.

• Bachelor's degree in Computer Science, Data Science, a related technical field, or equivalent practical experience.
• 2+ years of professional software development experience delivering production-quality applications or services.
• Experience owning and delivering features or services within an analytics, data, or product engineering domain, with responsibility for implementation, testing, troubleshooting, and maintenance.
• Proficiency in software engineering fundamentals, including programming, version control, automated testing, API development, data modelling, and system design.
• Bias to action and ownership, taking unclear problems from idea to a working, reliable solution quickly and working well with technical and non-technical teams.
• Foundational understanding of analytics and data, including metrics, data modelling, and how people use data to make decisions.
• Hands-on experience using AI-assisted development tools, and the habit of reviewing generated code for correctness, security, and maintainability.

• Experience building and operating analytics products or data-intensive services at meaningful scale, with attention to performance, availability, and maintainability.
• Demonstrated ability to contribute to architecture and technical design decisions within a service or multi-service product area.
• Experience using observability, automation, testing, and continuous delivery practices to strengthen operational excellence.
• Ability to use data and product signals to evaluate technical outcomes, identify improvement opportunities, and guide engineering decisions.
• Hands-on exposure to AI/ML-enabled products or workflows, such as agents, large language models, retrieval-augmented generation, or MCP tools, including integrating, evaluating, or operating AI-driven capabilities safely in production.
• An eye for analytics user experience, such as when to show uncertainty, cite a metric, or explain a change.
• Experience with front-end or full-stack development and building interactive, user-facing products.