Job Description
Business Overview
Rakuten Group, Inc., a global technology leader based in Japan, provides innovative solutions that enrich the lives of millions worldwide. Our Technology division is pioneering in leveraging cutting-edge technologies to deliver exceptional experiences across our services.

Department Overview
The importance of catalog data as a master data is increasing as it serves as a hub and data platform that connects products and corporate data handled within Rakuten services. By managing abundant and accurate catalog data and deploying it as master data across Rakuten services, it plays the role of core data that accelerates new service development.

The Data Knowledge Department (DKD) is committed to building data creation processes, collecting and expanding catalog data, and improving quality in order to continue providing services that meet and exceed the expectations of Rakuten's customers.

Under the mission of creating the best catalog data and improving user experience, we provide the power of Rakuten's design, technology, and operations to provide high-quality catalog data that is at the core of our business strategy. This contributes to Rakuten's vision of "Empowering people and society through innovation".

Position
Position Details
The main development task is to build Catalog Data Solution platform that enable DKD Data asset to be more usable and helping Rakuten business to unlock business opportunity using Catalog Data Asset. In addition to building platform to unlocking Catalog Data asset we also need to support our client to provide data that meets our clients needs.

One of the important challenges is to continuously add feature and also maintaining Platform and Data Quality of Enrichment Solution to our data client.

We also practice long-term product development as we handle the entire development process from design, development, to release and operation. We also play a role in considering a more scalable system design while preventing or paying technical debt.

Responsibilities

• Design, develop, and maintain efficient and scalable data pipelines using Apache Spark (primarily with Java) or Apache Beam or Kubeflow to ingest, process, and transform large datasets from various sources.
• Design and implement optimized data models for analytical and operational use cases, considering performance, scalability, and data integrity.
• Build and maintain web tools and applications in Python and Java.
• Develop end-to-end applications integrating with data infrastructure.
• Maintain infrastructure aligned with product and security requirements.
• Contribute to product development and architecture design.
• Ensure high performance and data quality.

Mandatory Qualifications

• Fluent in both Japanese and English, with exceptional communication skills across diverse, multinational environment.
• Minimum 3 years of proven experience in Data Engineering fields.
• Demonstrated experience building production-grade data pipelines.
• Track record of developing full-stack applications.
• Strong background in API design and development.
• Solid experience with Google Cloud Platform (GCP) services, including BigQuery, Dataflow, Dataproc, Cloud Storage, and Pub/Sub.
• Expertise in data pipeline development and ETL processes.

Desired Qualifications

• Understanding of AI/GenAI concepts and their data requirements is a plus.
• Experience building data pipelines to support AI/ML models is a plus.
• Data assets, data governance, storage, and sharing.
• Background in performance optimization.
• Experience as a mentor, tech lead or leading an engineering team.
• Have a strong ownership and able to own project end-to-end from designing to deployment.
• Experience in building and driving adoption of new products.
• Strong adaptability and commitment to continuous learning, keeping up with the latest advancements in technology.

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Languages
English (Overall - 3 - Advanced), Japanese (Overall - 3 - Advanced)