Data Engineer

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all. At eBay, we’re more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform connects millions of buyers and sellers around the world and creates economic opportunity for individuals, entrepreneurs, businesses, and organizations of all sizes. As we continue our tech-led reimagination of the global marketplace, we’re looking for people who are passionate about solving complex problems, building at scale, and creating experiences that make commerce better for everyone. About the team and role: We are looking for a talented and passionate Data Engineer to join our team and help build scalable, reliable data solutions that power critical experiences across eBay. In this role, you will architect, design, and develop high-performance real-time and batch data pipelines that process massive volumes of data. You will help build the infrastructure that enables real-time insights, analytics, and personalized experiences for millions of users across the eBay marketplace. You will work with modern data technologies including Kafka, Flink, Spark, Databricks, Airflow, dbt, and AWS, solving challenging engineering problems where scalability, performance, reliability, and data accuracy are essential. You will own projects throughout the engineering lifecycle and collaborate closely with Data Science, Product, and Engineering teams to translate complex requirements into robust data solutions. What you will accomplish: Build data pipelines at scale: Design, develop, and operate robust real-time and batch data pipelines using Kafka, Spark, and Flink to process large volumes of data efficiently and reliably. Develop reliable data workflows: Build and manage complex workflow orchestration using Airflow, ensuring data is processed accurately and available when downstream systems and teams need it. Improve data quality and trust: Implement and maintain automated data quality and validation frameworks using technologies such as Monte Carlo, Great Expectations, or similar tools. Build cloud-native data solutions: Leverage Databricks and AWS to develop scalable data platforms and services that improve developer productivity, system performance, and operational efficiency. Develop scalable transformations: Build maintainable data transformation workflows using dbt and establish reusable patterns for producing high-quality datasets. Own projects end-to-end: Drive major data engineering initiatives from architecture and design through implementation, testing, deployment, monitoring, and long-term production support. Solve complex scalability challenges: Identify technical risks, performance bottlenecks, and future scalability challenges and proactively recommend architectural improvements. Collaborate across teams: Partner with Data Science, Product, Analytics, and Engineering teams to understand data requirements, influence technical roadmaps, and deliver solutions that create meaningful customer and business impact. Raise the engineering bar: Advocate for continuous improvement of our data architecture, development practices, tooling, and technology stack. Build for long-term maintainability: Develop reusable libraries and engineering patterns while maintaining clear documentation for critical systems,