Unified Data Platform Architect

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• Define the architecture and long-term technical direction for large-scale data platforms and distributed processing systems.
• Lead the design of highly scalable, reliable backend and data infrastructure supporting business-critical workloads.
• Architect and build high-performance backend services and platform components primarily in Java, with Python used where appropriate for data processing, orchestration, automation, and tooling.
• Design and evolve large-scale batch and real-time data processing architectures using Apache Spark and Apache Flink.
• Establish architectural patterns for data ingestion, transformation, computation, orchestration, storage, and serving across the data lifecycle.
• Design reliable workflow and dependency-management capabilities using Apache Airflow and related orchestration technologies.
• Define architecture and operational patterns for running large-scale data and backend workloads on Kubernetes.
• Solve complex distributed-systems challenges involving scalability, state management, fault tolerance, consistency, partitioning, backpressure, resource management, and recovery.
• Drive improvements in platform reliability, performance, observability, developer productivity, and infrastructure efficiency.
• Identify systemic bottlenecks and lead architectural initiatives that improve throughput, latency, availability, and cost at scale.
• Establish technical standards and reusable platform capabilities that enable multiple engineering and data teams.
• Lead architecture reviews and provide technical guidance for high-impact initiatives spanning multiple systems and organizational boundaries.
• Partner with architects, senior engineers, engineering leaders, product teams, data engineers, and infrastructure teams to translate business requirements into long-term technical strategy.
• Mentor senior engineers and raise the technical bar through design reviews, code reviews, technical guidance, and engineering best practices.
• Evaluate emerging technologies and make strategic build-versus-buy and architectural decisions for the data platform.
• Lead complex migrations and modernization initiatives while maintaining production reliability and minimizing disruption to dependent systems.
• 10+ years of software engineering experience, including significant experience designing and operating large-scale distributed systems or data platforms.
• Deep expertise in Java and strong software engineering fundamentals.
• Proficiency with Python for data engineering, automation, or platform development.
• Extensive experience designing and building production backend services and distributed systems.
• Deep hands-on experience with Apache Spark and large-scale distributed data processing.
• Strong experience with the Hadoop ecosystem, including technologies such as HDFS, Hive, and YARN.
• Experience designing and operating real-time or stateful streaming systems using Apache Flink.
• Experience designing large-scale workflow orchestration using Apache Airflow or comparable technologies.
• Strong production experience with Kubernetes, containers, and cloud-native application architectures.
• Deep understanding of distributed-systems concepts including partitioning, replication, consistency, fault tolerance, distributed state, scheduling, resource management, and failure recovery.
• Strong understanding of both batch and streaming architectures and the tradeoffs between different processing models.
• Demonstrated experience driving architecture and technical decisions across multiple teams or major platform initiatives.
• Proven ability to operate effectively in ambiguous problem spaces and turn broad business or platform requirements into executable technical strategies.
• Track record of mentoring senior engineers and influencing engineering practices beyond an immediate team.
• Experience architecting platforms processing petabyte-scale datasets and/or billions of events per day.
• Deep knowledge of Apache Spark and Apache Flink.
• Experience with modern data lake and lakehouse technologies such as Apache Iceberg
• Experience designing multi-tenant data platforms, including workload isolation, resource governance, capacity management, and cost optimization.
• Strong knowledge of data formats, partitioning strategies, schema evolution, metadata management, and data lifecycle management.
• Experience with data governance, lineage, data quality, and platform observability.
• Experience designing highly available control-plane or platform services supporting large numbers of internal users and workloads.

eBay is an equal opportunity employer.

All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status.

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