Software Data Engineer - Java / Scala

To excel as a Software Data Engineer at eBay, candidates should have extensive experience with technologies like Apache Kafka and Apache Flink, alongside proficiency in the Hadoop ecosystem, including Hive, Iceberg, and Spark. Experience in automating data pipelines and strong programming skills in Java, Scala, Python, or similar languages are essential. Candidates should also have solid understanding of SQL and database management.

As a Software Data Engineer at eBay, your key responsibilities will include designing and enhancing data architecture for optimal scalability, automating data pipelines, classifying and managing data access, and collaborating with cross-functional teams. You will also engage in innovative research to adopt new technologies that improve our data processes.

In the role of Software Data Engineer at eBay, you'll work with a range of technologies, including Apache Kafka, Apache Flink, and the Hadoop ecosystem. Familiarity with tools like Apache Airflow for workflow orchestration, as well as SQL databases such as Oracle or SQL Server, will be important to succeed in this position.

By taking on the role of Software Data Engineer at eBay, you will contribute to our mission of reinventing the future of eCommerce. Your expertise will help enhance data management systems that connect buyers and sellers worldwide, paving the way for new business opportunities and ensuring a seamless customer experience.

At eBay, the culture for a Software Data Engineer is collaborative and inclusive. Team members are encouraged to share their unique ideas and perspectives in a supportive environment. We believe in authenticity, continuous learning, and pushing boundaries to stay ahead in the dynamic world of eCommerce.

When encountering data pipeline failures, I first check the logs to identify the root cause. Then, I prioritize resolving the issue by implementing necessary fixes and updates, while also developing monitoring systems to prevent similar failures in the future. It’s crucial to have a robust error handling mechanism in place for real-time feedback.

Batch processing involves processing data in large chunks at scheduled intervals, which is efficient for handling large datasets at once. In contrast, real-time processing involves continual input and output of data as it is generated, allowing for immediate insights. The choice between them often depends on the specific use case and business requirements.

To ensure data compliance and security, I classify data based on its sensitivity, establish access control protocols, and implement encryption methods for sensitive information. Regular audits and adherence to regulatory requirements are also part of my strategy to maintain data integrity.

I stay updated with the latest trends in data engineering by following industry-leading publications, attending webinars and conferences, and being active in online communities. I also regularly experiment with new tools and technologies to evaluate their potential benefits for data solutions.

One challenging project involved migrating existing data architectures to a more scalable solution using cloud-based technologies. I led the team through the planning and execution phases, ensuring we effectively managed data flow and met performance benchmarks while simultaneously addressing compliance requirements.

I have extensive experience using Apache Kafka for building real-time data pipelines. I’ve utilized it for message brokering, ensuring fault tolerance and seamless scalability. Implementing Kafka has allowed me to efficiently manage large streams of data, enabling real-time analytics and faster decision-making.

When facing conflicts in a team environment, I believe clear communication is key. I focus on understanding different perspectives and finding common ground. If needed, I facilitate discussions to mediate issues, ensuring that all voices are heard so that we can collaboratively find a solution.

I prefer using Apache Airflow for orchestrating data workflows due to its flexibility and ease of use. With its DAG (Directed Acyclic Graph) structure, I can easily manage and schedule complex workflows, which is crucial for maintaining efficient data processing pipelines.

I typically track metrics such as throughput, latency, error rates, and resource utilization (CPU and memory). By analyzing these metrics, I can identify bottlenecks, enhance pipeline performance, and ensure that data is processed efficiently and reliably.

I am excited about the opportunity to work as a Software Data Engineer at eBay because of its commitment to innovation in eCommerce and the chance to contribute to a global platform. I admire eBay's culture of collaboration and authenticity, and I am eager to leverage my skills to help improve data processing solutions that have a meaningful impact.