About the Team:

At eBay, the Data Platform Engineering group designs and scales innovative infrastructure and analytics solutions that power robust data flows, real-time insights, and inventory mapping globally. The team builds and maintains large-scale systems supporting high-volume data acquisition, transformation, and reporting, driving platform reliability and supporting business decision-making across regions.

Key Responsibilities:

- Architect, develop, and maintain backend services and distributed systems driving large-scale data collection, processing, and reporting workflows.

- Design and optimize ETL (Extract, Transform, Load) pipelines for a range of data sources, enabling inventory mapping, enrichment, and transformation.

- Build, monitor, and troubleshoot microservices, data crawling tools, and plugins for cloud-native deployments and high system resilience.

- Implement advanced extraction and mapping frameworks using modern technology stacks, ensuring data is clean, comprehensive, and actionable.

- Ensure platform reliability and scalability with proactive incident response, robust monitoring, and ongoing technical upgrades.

- Contribute to customer-facing portals and dashboards that support internal teams in configuring, managing, and monitoring data workflows.

- Collaborate globally across engineering, product, and analytics teams to deliver impactful platform features and ensure alignment on technical best practices.

- Participate actively in release management, code reviews, operations, and knowledge sharing for continual improvement and technical excellence.

- Guide peers in troubleshooting, system design, DevOps processes, and cloud pipeline architecture.

Tech Stack:

- Backend: Java (Spring Boot), Groovy, Python, Spark, Hadoop

- Databases & Big Data: MongoDB, MySQL, Oracle, Hive

- Frontend: HTML, JavaScript, Angular

- Infrastructure & Tools: ElasticSearch, Kafka, Docker, Kubernetes, Grafana

Qualifications:

- Bachelors or advanced degree in Computer Science, Software Engineering, or a closely related field.

- Professional experience in large-scale data systems, backend software engineering, and analytics platforms.

- Exposure to business data workflows, infrastructure automation, or external data aggregation is a plus.