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About the Role
The Data Platform Department (DPD) at Rakuten Group develops and maintains a comprehensive data platform, empowering over 70 Rakuten services with solutions for data ingestion, discovery, governance, analytics, and querying. We support data-driven decision-making across one of Japan's largest data ecosystems, providing the tools and infrastructure to support key domains such as Data Lakes, Data Warehouses, and Business Intelligence.
Responsibilities
- Design, develop, test, and implement scalable, cloud-based data warehouse solutions, optimized data pipelines, and robust data modeling.
- Maintain a healthy data warehouse ecosystem through comprehensive infrastructure management, ensuring seamless data integration and proactively addressing performance bottlenecks.
- Adapt to and utilize Rakuten’s internal data pipeline orchestration framework, built on top of GCP-managed Airflow.
- Collaborate with cross-functional teams to identify data requirements and align them with business objectives.
- Maintain comprehensive documentation, including platform architecture, data dictionaries, infrastructure configurations, and operational processes.
- Work closely with end-users to understand requirements, gather feedback, and deliver impactful, customized reports.
- Propose and present visualization solutions to relevant stakeholders.
- Architect reporting-specific data marts to ensure high-performance dashboards.
- Develop, maintain, and optimize data pipelines, including DAG scheduling, data ingestion, and ETL/ELT processes.
- Validate and ensure the accuracy, consistency, and reliability of all data used in visualizations.
- Design, build, and manage interactive reports using modern data visualization tools.
Requirements
- B.S. in Computer Science, or a related field, or equivalent education and experience.
- 3+ years of end-to-end experience in data engineering (SQL) and operational experience in cloud-based or Linux systems.
- Experience in developing and building robust data warehouse ecosystems using Google Cloud Platform (GCP – BQ, GCS), Python, and shell scripting.
- Understanding of data warehouse/big data concepts, data modeling, batch-processing pipelines (ETL), and data integration techniques for managing large volumes of data.
- Hands-on experience creating and leveraging data visualization platforms (e.g., DOMO, Tableau, MicroStrategy, PowerBI, etc.).
- Experience with Git.
- Experience with Atlassian tools (Confluence, Jira, BitBucket).
- Experience working on streaming data pipelines using tools such as Airflow, Apache Kafka, or Spark Streaming.
- Experience managing systems with high Service Level Objectives/Agreements (SLOs/SLAs).
- Effective communication and project management skills.
- Quick learner, highly adaptable, and committed to continuous learning.
- Strong problem-solving and solution-creation mindset.
- Detail-oriented and driven by excellence.
- A "Can-Do" attitude and willingness to embrace new challenges.
- Strong teamwork and collaboration capabilities.
Skills
- Data Engineering
- SQL
- Cloud-based systems
- Linux systems
- Google Cloud Platform (GCP – BQ, GCS)
- Python
- Shell scripting
- Data warehouse concepts
- Big data concepts
- Data modeling
- Batch-processing pipelines (ETL)
- Data integration
- Data visualization platforms (DOMO, Tableau, MicroStrategy, PowerBI)
- Git
- Atlassian tools (Confluence, Jira, BitBucket)
- Streaming data pipelines (Airflow, Apache Kafka, Spark Streaming)
- Communication
- Project management
- Problem-solving
- Teamwork
- Collaboration
Experience Level
- 3+ years of end-to-end experience in data engineering
Education Level
- B.S. in Computer Science, or a related field, or equivalent education and experience
About the Company
- Rakuten Group develops and maintains a comprehensive data platform, empowering over 70 Rakuten services with solutions for data ingestion, discovery, governance, analytics, and querying.
- We support data-driven decision-making across one of Japan's largest data ecosystems, providing the tools and infrastructure to support key domains such as Data Lakes, Data Warehouses, and Business Intelligence.