Data Engineer II

• Design, build, test, and maintain scalable, resilient, and secure data pipelines, data services, and storage layers that power Expedia Group products and platforms.

• Collaborate with product managers, data scientists, analysts, and other engineers to translate business and customer requirements into robust data system designs, including low-level design (LLD), API design, and data models for batch and real-time use cases.

• Develop and optimize data ingestion, transformation, and serving workflows, writing clean, maintainable, and well-documented code, along with automated tests and tooling that improve reliability, observability, and data quality.

• Participate in code reviews, design reviews, and technical discussions, identifying opportunities to simplify data systems, reduce technical debt, and improve performance, scalability, and cost efficiency across multiple datasets or services.

• Own the end-to-end lifecycle of data features and services, including deployment, monitoring, incident response, and incremental improvement, and safely integrate and operate AI/ML‑enabled solutions that improve outcomes.

• Work across different parts of the data stack and adjacent domains as needed, demonstrating familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products.

Minimum Qualifications:

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

• 2+ years of relevant professional experience.

• 2+ years of relevant professional experience designing, building, and operating production data pipelines, data services, or data platforms.

• Hands-on proficiency in at least one modern programming language and its ecosystem, with experience in system design (LLD), API design, and data modeling for data-centric, service-oriented, or microservices architectures.

  • Proven experience owning data features or services through development, testing, deployment, and operational support, including monitoring, troubleshooting, and resolving production issues while ensuring data quality, reliability, and secure data handling.