Data Engineer I - Licensing

As a Data Engineer I at Kelp, you will be responsible for scoping, building, and enhancing data pipelines and API infrastructure. You will manage and process vast amounts of data from Spotify's extensive user base, implement quality control measures to improve data and API integrity, and share on-call responsibilities for monitoring production systems.

To qualify for the Data Engineer I position at Kelp, candidates should hold a Bachelor's or Master's Degree in a relevant field. It is also essential to have a solid understanding of system design, data structures, and algorithms, and experience in building backend services to support the essential work you’ll do here.

A Data Engineer I at Kelp will engage with a variety of modern technologies, including Scala, Scio, Java, Flink, and Google Cloud Platform. Familiarity with these tools is crucial for successfully processing the enormous volumes of data from Spotify and improving the overall quality of data products.

In your capacity as a Data Engineer I at Kelp, you will play a pivotal role in enabling product innovation by ensuring that Spotify has the content rights necessary for launching new offerings. Your work on data pipelines and API enhancements will provide critical insights that guide decision-making and strategic direction.

At Kelp, the work environment is flexible and fosters collaboration. You will have the autonomy to tackle loosely defined problems, work alongside skilled colleagues from various disciplines, and contribute meaningfully to team decisions—all while enjoying the support of a company that values innovation and employee well-being.

Draw from specific projects where you built or optimized data pipelines, detailing the technologies used, challenges faced, and how you overcame them. Highlight your problem-solving skills and attention to detail throughout the process.

Discuss different testing methodologies, potential data validation frameworks, and your approach to troubleshooting data-related issues. Highlight the importance of maintaining high data integrity in your previous work.

Share your experience using Google Cloud or other cloud platforms, emphasizing the tools you’ve used, any projects you've completed, and how cloud computing has enhanced your data engineering workflows.

Explain your communication style, how you incorporate feedback from team members, and what you believe makes an effective team collaboration. Consider citing examples from past projects.

Discuss your method for assessing project urgency and importance. Talk about how you use tools and frameworks to manage timelines and expectations, ensuring quality work under pressure.

Describe a specific problem, the context in which it occurred, and the strategies you implemented to resolve it. Focus on your analytical skills and how you leveraged your knowledge of data engineering.

Demonstrate your comprehension by discussing the principles of system design relevant to data engineering, including scalability, reliability, and performance. Provide examples where you applied these principles.

Share the resources you use for learning, whether it’s online courses, webinars, or participating in community discussions. Highlight how you apply new knowledge to your projects.

Express your belief in the vital role of testing throughout data engineering, discussing how it ensures the accuracy and reliability of systems. Mention specific types of testing you prefer and why.

Share your enthusiasm for joining Kelp, emphasizing your alignment with their mission and values. Discuss how the role fits in with your career goals and what you hope to contribute and gain.