Data Engineer, Data Insights

As a Data Engineer at PayPay, you'll be responsible for designing, developing, and maintaining scalable data ingestion pipelines using tools such as AWS Glue and Step Functions. You will also optimize large-scale data pipelines to ensure their performance and efficiency, implement data processing workflows using technologies like Hudi and Spark, and collaborate with cross-functional teams to ensure comprehensive data integration across the organization.

Candidates for the Data Engineer role at PayPay should have a minimum of 5 years of experience in data engineering or a related field. Proficiency in technologies such as Apache Hudi, Delta Lake, and Spark is crucial. Additionally, a deep understanding of AWS services, including Lambda and Glue, along with experience in Data Orchestration tools is expected. Familiarity with Python, SQL, and infrastructure management using Terraform will also set you apart.

At PayPay, Data Engineers utilize a range of advanced tools and technologies. You'll work extensively with AWS services like Glue, Lambda, and EMR for data processing. Additionally, skills in using Apache Hudi, Delta Lake, Spark, as well as knowledge of tools such as Airflow and data warehousing technologies like AWS Athena and BigQuery will be integral to your role.

Yes, the Data Engineer position at PayPay is completely remote. We embrace a Work From Anywhere policy, allowing team members to work flexibly from any location in Japan. This setup is designed to promote work-life balance and accommodate individual needs.

The working culture for Data Engineers at PayPay is dynamic and innovation-driven. We emphasize collaboration and creativity, encouraging our team members to challenge themselves and push boundaries. With our Super Flex Time policy, there is flexibility in work hours, allowing you to create a schedule that works best for you while pursuing exciting data projects.

When answering this question, detail the specific AWS services you've used, like Glue and Lambda. Share how you've implemented these services in your projects, and emphasize your understanding of scalability and performance optimization in data pipelines.

Discuss your methodology for identifying bottlenecks in data pipelines. Include specific tools and techniques you've used to enhance performance and reliability, such as employing best practices in data processing and regularly monitoring pipeline efficiency.

Provide an overview of your hands-on experience with both technologies. Highlight projects where you used Hudi or Delta Lake to manage data efficiently, focusing on features like support for streaming data and data versioning.

Share your strategies for data validation and error handling. Discuss how you implement testing and monitoring throughout the data pipeline, ensuring that the data processed adheres to quality standards.

Use the STAR method (Situation, Task, Action, Result) to describe a specific project. Focus on the challenges you faced, your approach to solving them, and the eventual success of the project, including what was learned.

Explain how Data Engineers are critical in providing clean, reliable data for analysis and reporting. Discuss your view on collaborating with data scientists and analysts to ensure data pipelines support business needs effectively.

Detail your experience with data cataloging tools like AWS Glue Data Catalog or Lakeformation. Emphasize the importance of metadata management and how you've worked to improve data discoverability in previous roles.

Talk about your time management skills and any tools or methodologies you apply for project tracking. Highlight your ability to assess project urgency and importance, allowing you to allocate your time efficiently.

Discuss your understanding of data governance principles, including compliance and security. Mention specific practices like auditing, monitoring access, and educating team members on data stewardship.

Explain your approach to cross-team collaboration, noting the importance of communication and mutual respect. Share how you've built relationships with other teams to facilitate information sharing and streamline processes.