Scale

Overview

We are looking for a Data Engineer III who combines strong data engineering fundamentals with solid hands-on experience in Apache Spark, workflow orchestration, and modern data platform development to design and operate data products that power metric computation, analytics, and AI-assisted workflows.

About Scale

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.

Requirements & Eligibility

• 5+ years of data engineering experience building production-grade data solutions and platforms at scale.
• Experience with batch processing technology
• Strong proficiency in SQL, including complex joins, window functions, query tuning, and performance optimization across distributed compute engines.
• Hands-on experience with Apache Spark and the ability to tune and troubleshoot Spark workloads in production.
• Experience with big data and lakehouse technologies such as Spark, Databricks, Trino/Presto, Iceberg or Delta Lake, and orchestration tools such as Airflow.
• Experience designing and maintaining data models for analytics and BI, including star or snowflake schemas, aggregate tables, and semantic or metric layers.

Key Responsibilities

• Design, build, and operate scalable, reliable data pipelines across large-scale distributed data platforms.
• Develop and optimize Spark-based data processing jobs, with a strong understanding of performance tuning, partitioning, joins, shuffles, and troubleshooting production issues.
• Write and optimize advanced SQL across engines such as Spark SQL, Trino/Presto, and similar distributed query systems for large-scale transformations, aggregations, and analytics use cases.
• Build and maintain analytics-ready data models including fact tables, dimensions, aggregates, metric layers, and gold datasets used by dashboards, experimentation platforms, and executive reporting.
• Build and maintain Airflow workflows for dependable scheduling, monitoring, recovery, and operational support.
• Contribute to data access services, APIs, or integration interfaces that help downstream applications and tools consume trusted metrics and datasets.
• Develop data quality, observability, and alerting capabilities that improve operational reliability across production workflows.