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.
About the team and the role:
Cloud Data Technologies (CDT) team at eBay is responsible for all the data infrastructure and management of E2E data lifecycle for all eBay data from onsite to analytics use cases. We are looking for a senior software engineer deeply passionate about data-driven technologies and architectures.
The ideal candidate will have a robust background in streaming technologies such as Apache Kafka and Apache Flink, coupled with extensive experience in lakehouse architecture. This role demands a strong understanding of data semantics, data discovery, pipeline automation, materialized views, and subsumption within large-scale data environments.
What you will accomplish:
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Architectural Leadership: Design and enhance our data architecture to support real-time and batch processing with a focus on scalability and fault tolerance using Kafka, Flink, and lakehouse principles.
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Data Semantics and Discovery: Implement systems and procedures for effective data semantics management, ensuring data is accurately categorized and easily discoverable.
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Pipeline Automation: Develop and maintain automated data pipelines that ensure efficient data flow and processing from multiple sources to our lakehouse architecture.
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Data Consumption Optimization: Create strategies and systems for optimal generation of materialized views and data subsumption to ensure that our data architecture remains cutting-edge, minimizes redundancy, and achieves required level of performance.
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Cross-functional Collaboration: Work closely with data scientists, business analysts, and other engineering teams to define and refine requirements that drive data solutions.
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Innovation and Research: Stay abreast of the latest industry developments in data engineering and propose adoption of new technologies or methodologies to keep our data infrastructure ahead of the curve.
What you will bring:
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Extensive experience with Apache Kafka, Apache Flink, and other relevant streaming technologies.
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Deep understanding of lakehouse architecture and its implementation in large-scale environments.
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Strong knowledge of data semantics, discovery processes, and data governance.
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Proven ability to design and implement automated data pipelines and materialized views.
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Experience with data subsumption techniques and tools.
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Proficiency in Java, Scala, Python, or similar languages.
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5+ years of directly applicable experience
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