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Protocol Intelligence
Data-driven signals on your job's competitivenessTier‑1 brand, popular Data Engineer title, metro location, and broad skill requirements increase competition.
Core data engineering skills are broadly transferable across industries with low domain bias.
Minimum years plus required production data pipeline and programming experience imply medium strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable, resilient, and secure data pipelines, data services, and storage layers that support Expedia Group products and platforms.
Own end-to-end lifecycle of data features/services including deployment, monitoring, incident response, and incremental improvement, particularly for AI/ML-enabled solutions.
Collaborate cross-functionally with product managers, data scientists, analysts, and engineers to translate business needs into robust data system designs and APIs for batch and real-time use cases.
Minimum Requirements
Bachelor’s degree in Computer Science or related technical field, or equivalent professional experience.
2+ years of relevant professional experience in designing, building, and operating production data pipelines or data services.
Hands-on proficiency in at least one modern programming language, including experience with low-level system design, API design, and data modeling for data-centric or microservices architectures.
Experience owning data features or services through development, testing, deployment, monitoring, and production incident resolution.
Ideal Candidate Profile
Experienced in designing and implementing scalable, fault-tolerant, high-throughput data solutions with well-structured schemas and APIs serving multiple teams or domains.
Skilled in improving data platform availability, performance, and reliability via observability, automation, alerting, and automated recovery.
Familiar with AI/ML-enabled platforms and integrating AI-driven data features into production environments, with ability to contribute to data architecture decisions and evolving engineering standards for responsible data and AI/ML usage.