Software Engineer, Data Platform

As a Software Engineer in our Data Platform team, your primary responsibilities will include designing, developing, and maintaining scalable components of the Data Platform service. You'll work closely with cross-functional teams to gather requirements and implement effective solutions, focusing on optimizing system performance, scalability, and reliability while troubleshooting complex issues in distributed systems.

The ideal candidate for the Software Engineer position at the Data Platform team should possess strong experience with Kubernetes, Helm, and Go programming. Familiarity with cloud platforms, particularly Azure, along with experience in data warehousing technologies such as Iceberg and Trino, is essential. Additional experience with Kafka, CI/CD tools, and Linux-based infrastructure will further enhance your candidacy.

A Software Engineer at our Data Platform team should be proficient in Go programming language, which is crucial for developing and maintaining our Data Platform service. In addition, familiarity with Shell or Bash scripting is beneficial to navigate various backend processes efficiently.

In the role of a Software Engineer at the Data Platform team, you'll work with cutting-edge technologies including Kubernetes, Kafka, Flink, and Spark. You'll also engage with advanced cloud platforms, data warehousing tools like Iceberg and Polaris, and observability tools such as Kloudfuse and Grafana to enhance our data processing capabilities.

Yes, the Software Engineer position at our Data Platform team is a fully remote role available to candidates anywhere in the USA. This allows you to work flexibly from the comfort of your home, while still being part of a collaborative and dynamic team.

In your response, discuss specific projects where you used Kubernetes for container orchestration, highlighting challenges you faced and how you overcame them to ensure application reliability in a distributed environment. Be sure to detail any experiences with Helm or Operators for easier management of your applications.

Share your method for effective task prioritization, including utilizing tools like Agile methodologies or Kanban boards. Emphasize how you assess urgency, the impact of components on overall system performance, and collaboration with team members to ensure that key deadlines are met.

Discuss specific techniques you've applied in past roles, such as load testing and performance tuning based on real-world data patterns. Mention tools that assist in observability and monitoring, illustrating how you use data to make informed decisions that enhance system performance.

Highlight your knowledge and experience with data warehousing solutions, explaining how you have leveraged Iceberg or Trino in previous projects. Share specific use cases that demonstrate your ability to optimize query times and manage large sets of data efficiently.

Walk the interviewer through your troubleshooting process, from gathering logs and metrics to isolating the problem area. Provide examples that showcase your analytical skills and resourcefulness in diagnosing and resolving complex system behavior.

Discuss your hands-on experience with CI/CD tools, particularly GitHub Actions, detailing how you have implemented automated testing and deployment pipelines in previous infrastructure projects. Highlight how this enhances code quality and delivery speed.

Mention any previous experience using observability tools like Kloudfuse and Grafana in enhancing the monitoring and performance of applications. Explain how effectively visualizing data helps you and your team identify bottlenecks and improve system reliability.

Elaborate on past projects where you utilized streaming technologies like Apache Flink, explaining how you designed and implemented data processing pipelines that handle real-time data efficiently. Provide examples that showcase your problem-solving skills in stream processing contexts.

Describe your communication style, particularly your approach to working collaboratively across teams. Highlight any specific tools or strategies you use for effective information sharing and ensuring alignment on project goals and timelines.

Outline your understanding of microservices architecture, discussing best practices such as API design, service decoupling, and ensuring failover strategies. Provide insights into how these practices have positively impacted previous projects.