楽天グループ株式会社：1027646　Data Engineer, Data Solution Group – Data Knowledge Department (DKD)

会社名

楽天グループ株式会社

会社概要

未来を信じ、より良い明日を創っていく。
イノベーションを通じて、人々と社会をエンパワーメントする。私たちは、そんな想いを大切に世界の人々に喜びと楽しさを届けます。
楽天は、E コマース、FinTech、デジタルコンテンツ、通信など、70 を超えるサービスを展開し、世界10 億以上のユーザーに利用されています。
これら様々なサービスを、楽天会員を中心としたメンバーシップを軸に有機的に結び付け、他にはない独自の「楽天エコシステム」を形成しています。ダイバーシティ推進は、楽天にとって最優先の企業戦略のひとつです。従業員の出身は70カ国・地域以上。世界中からユニークで多様な文化的背景や視点を持つ優秀な人材が集まり、イノベーションの原動力になっています。社内カフェテリアにはベジタリアン、ハラル対応のメニューを用意。礼拝所（Prayer room）もあります。
また、仕事と育児の両立支援や、障がい者雇用・活躍促進も積極的に推進。社内のLGBT（※1）当事者やアライ（※2）に対して、情報共有やサポート体制の強化も進めています。誰もが自分らしく力を最大限発揮して働ける。それが楽天のダイバーシティです。

70を超えるサービスを提供し、世界30カ国にサービス展開拠点を持ち、従業員の出身国・地域数は100を超え、オープンポジション制度を活用して多様なキャリアを描くことができる点も魅力です。
フレックスタイム制度、事情に応じたリモートワークの活用が可能です。本社には託児所やフィットネスジム、三食無料で利用可能なカフェテリアが併設されるなど、社員を支える環境が整備されています。

ポジション

1027646　Data Engineer, Data Solution Group - Data Knowledge Department (DKD)

仕事内容

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.

求める経験・スキル

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.