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.
We are looking for Software Engineering Manager to join the Golden Data Sets (GDS) Platform team within eBay's Core Data Platform organization.
The problem we're solving is significant: eBay currently manages over ten thousands of loosely defined datasets with fragmented pipeline implementations, resulting in widespread data duplication, redundant compute, and runaway operational costs. The GDS Platform addresses this by building a fully declarative data platform — one that accepts dataset specifications, maintains a comprehensive view of the total data flow graph, and automatically produces optimized pipelines to systematically eliminate these inefficiencies at scale.
As an engineer on this team, you will contribute to the systems that power this platform — from computation graph management to pipeline optimization and dataset lifecycle. Your work will directly reduce operational waste across eBay's data infrastructure.
Note: This is a Data Platform Engineering role. Familiarity with Spark, Flink, and the Hadoop ecosystem is useful context, but your primary responsibility is building the platform itself — not authoring pipelines.
What You’ll Do and Learn
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
What You Bring
Strong experience designing and building large-scale distributed systems or platforms (compute, storage, APIs, or orchestration systems)
Proven ability to own and deliver complex platform components end-to-end, from design to production
Systems thinking mindset with the ability to reason about data flow, dependencies, scaling bottlenecks, and reliability trade-offs
Experience building platform abstractions or frameworks, not just consuming them
Strong communication skills with the ability to drive alignment across cross-functional engineering teams
Curiosity and growth mindset to explore declarative paradigms, optimization systems, and emerging technologies
What We Bring
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
Qualifications
8+ years of experience in distributed systems, platform engineering, or data infrastructure
5+ years of experience as people manager
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
