Expedia

Overview

As a Data Engineer III on M&I Forecasting, you will own and scale the data foundations behind ad inventory forecasting. You’ll design, build, and operate pipelines, analytics layers, and impression data models that power forecast accuracy measurement, pacing analysis, and delivery risk detection across global markets.

About Expedia

Expedia Group includes three flagship consumer brands - Expedia, Hotels.com, and Vrbo - along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease.

Requirements & Eligibility

• Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
• 5+ years of relevant professional experience
• Proficiency in at least one modern programming language used for data engineering (such as Java, Scala, Python, or similar), along with strong skills in SQL and data modeling for analytical and operational use cases.
• Hands-on experience with data processing frameworks and storage technologies (for example, distributed processing, stream processing, and large-scale data warehouses or data lakes).
• Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products.

Key Responsibilities

• Design, build, and enhance scalable, secure, and reliable data pipelines and services that enable analytics, experimentation, and product features across multiple business domains.
• Develop, optimize, and maintain batch and streaming data processing solutions, including data modeling, schema evolution, and API design.
• Implement robust data quality, validation, monitoring, and observability practices to ensure accuracy, completeness, and timely availability of data for stakeholders.
• Collaborate with engineers, product managers, analysts, and data scientists to translate business and analytical requirements into well-architected data solutions and reusable components.
• Apply and extend engineering best practices for performance, reliability, and cost efficiency, including testing, CI/CD, deployment automation, and operational runbooks for data systems.
• Safely integrate and operate AI/ML‑enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows.