MTS 2, Software Engineer, Data Platforms

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

Perks and Benefits

• 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