Data Engineer III

Expedia•Seattle, WA

4d•Hybrid

About The Position

At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business. Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere. Introduction to the Team Looking to join a team that builds the data and platform foundations behind analytics, AI, reporting, and better decision-making at scale? As a Data Engineer III in the AI and Analytics Platform team, you will design and improve reliable data pipelines, shape well-modeled data products, and help build analytics-friendly and AI-ready datasets that power self-service insights, automation, and emerging Agentic AI use cases across Expedia Group. This role is a strong fit for someone who enjoys solving complex data problems, writing high-quality code and SQL, and building systems that are scalable, observable, and trusted by their users. In this role, you will Design, build, and maintain scalable batch and near real-time data pipelines and data products that support analytics, reporting, and downstream business use cases. Help build foundational analytics- and AI-friendly datasets across domains so data is easier to discover, trust, reuse, and apply in reporting, decision-making, automation, and emerging AI use cases. Write efficient, maintainable code and advanced SQL, applying strong engineering fundamentals to data ingestion, transformation, modeling, and access patterns. Lead complex, well-defined projects from technical design through delivery, while identifying inefficiencies in existing systems and improving reliability, scale, and maintainability. Build and improve data models, schema design patterns, and data quality practices so that data is easier to understand, trust, and use. Strengthen operational excellence through testing, monitoring, alerting, troubleshooting, and clear ownership of service health, freshness, and performance expectations. Partner closely with Product Managers, engineers, analysts, and business stakeholders to translate intake requests and business needs into practical technical solutions, with many projects and data products ultimately consumed by executive leadership for decision-making and visibility. Mentor more junior engineers through collaboration, technical guidance, and code review.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field; or equivalent related professional experience.
5+ years of relevant professional experience in data engineering, software engineering, or a closely related technical field.
Strong SQL skills and hands-on experience building and operating production-grade data pipelines or data products in cloud-based environments.
Experience with data modeling, schema design, and modern data access patterns, with the ability to make sound technical decisions within project scope.
Experience testing, monitoring, debugging, and improving data systems for reliability, observability, and operational quality.

Nice To Haves

Experience supporting analytics, business intelligence, experimentation, or AI/ML-oriented data use cases.
Experience with schema evolution, backfills, data freshness expectations, and service-level thinking for production data systems.
Experience leading moderately complex technical designs and driving delivery across multiple collaborators or dependent teams.
Experience improving engineering standards through mentoring, code reviews, documentation, and sharing best practices with peers.
Familiarity with cloud-native data tooling and distributed data processing patterns at scale.
Hands-on experience with technologies and infrastructure commonly used for end-to-end data engineering at Expedia, such as SQL, Python or Java, Spark, Hive, Trino, Querybook, Airflow, Jupyter, Databricks, AWS S3, AWS Glue Catalog, Unity Catalog, Dataproc, EGMP, Kafka Connect, DataPull, and Data Workbench.
Practical experience using AI-assisted engineering tools such as Claude, Codex, Glean, Cursor, or similar tools to accelerate development, troubleshooting, documentation, and knowledge discovery.
Ability to apply AI tools thoughtfully to enhance and automate data engineering workflows while maintaining strong standards for data quality, security, governance, and operational reliability.

Responsibilities

Design, build, and maintain scalable batch and near real-time data pipelines and data products that support analytics, reporting, and downstream business use cases.
Help build foundational analytics- and AI-friendly datasets across domains so data is easier to discover, trust, reuse, and apply in reporting, decision-making, automation, and emerging AI use cases.
Write efficient, maintainable code and advanced SQL, applying strong engineering fundamentals to data ingestion, transformation, modeling, and access patterns.
Lead complex, well-defined projects from technical design through delivery, while identifying inefficiencies in existing systems and improving reliability, scale, and maintainability.
Build and improve data models, schema design patterns, and data quality practices so that data is easier to understand, trust, and use.
Strengthen operational excellence through testing, monitoring, alerting, troubleshooting, and clear ownership of service health, freshness, and performance expectations.
Partner closely with Product Managers, engineers, analysts, and business stakeholders to translate intake requests and business needs into practical technical solutions, with many projects and data products ultimately consumed by executive leadership for decision-making and visibility.
Mentor more junior engineers through collaboration, technical guidance, and code review.