Requirements

8+ years of experience in distributed systems, platform engineering, or data infrastructure
Strong proficiency in Java or Python, with experience building production-grade systems
Experience with CI/CD, testing, and containerized environments
Solid understanding of distributed system design, algorithms, and scalability patterns
Familiarity with technologies such as Spark, Flink, or similar systems
Experience working with large-scale data ecosystems or data platforms is a plus
BS/MS in Computer Science or equivalent practical experience

What You'll Be Doing

Build and own core components of eBay’s declarative data platform, enabling automated generation and optimization of data pipelines at scale
Design and evolve systems that manage the global data flow graph, including dataset definitions, dependencies, and execution planning
Develop platform capabilities for computation graph management, pipeline optimization, and dataset lifecycle orchestration
Engineer solutions that systematically reduce data duplication, redundant compute, and operational inefficiencies across thousands of datasets
Contribute to long-term platform architecture through design reviews and architecture documents, ensuring scalability, correctness, and resilience
Build platform systems that balance performance, cost efficiency, and data correctness, while enforcing governance and compliance requirements
Drive operational excellence for platform services, including observability, reliability, and incident response
Collaborate with product, infrastructure, and data teams to standardize dataset definitions and improve platform adoption
Develop automation and intelligent tooling to improve platform efficiency, including opportunities to leverage AI/agent-driven optimizations
Learn and deepen expertise in areas such as declarative systems, distributed computation graphs, data governance, and large-scale platform engineering

Opportunity to solve foundational data platform challenges at massive scale, impacting thousands of datasets and pipelines
High-impact work focused on eliminating inefficiencies and reducing operational cost across the data ecosystem
Deep technical challenges in declarative systems, graph-based execution, and large-scale optimization
Collaborative and inclusive culture with strong emphasis on engineering excellence and knowledge sharing
Supportive environment with focus on sustainable operations, on-call balance, and long-term growth