Data Analyst, Rakuten Kobo

• Bachelor’s degree in Computer Science, Data Science, or related field
• 5+ years of experience in data analytics, analytics engineering, or BI, with demonstrated ownership of analytics data pipelines and models
• Strong SQL skills, including complex joins, window functions, and working with semi‑structured or nested data
• Solid understanding of data modeling and designing tables for downstream analytics and reporting use cases
• Strong analytical thinking, problem‑solving, and communication skills
• Experience working with modern cloud data warehouses (BigQuery preferred)
• Hands‑on experience with Dataform (experience with dbt is a plus)
• Experience with BI and visualization tools such as Tableau, Looker, or Domo
• Familiarity with analytics engineering best practices (e.g., testing, CI/CD for data)

Desirable

• If you’re looking for a company that inspires passion, personal, and professional growth – join Kobo and come help us on our mission of making reading lives better
• Rakuten Kobo Inc. is seeking an experienced Data Analyst to join our Data Storytelling & Insights team, working closely with our engineering foundations
• This role blends strong technical ownership of analytics‑ready data with the ability to translate complex information into clear, actionable insights
• You’ll work end‑to‑end across the analytics lifecycle—building scalable, trusted data models and partnering with stakeholders to deliver dashboards and analyses that inform key business decisions
• Design, build, and maintain analytics‑ready data models and pipelines using Dataform and BigQuery, including migrating and modernizing legacy logic into clearer, more reusable, and better governed data assets
• Ensure analytics code and pipelines are reliable, well‑documented, and suited for downstream reporting, experimentation, and analysis
• Build and maintain Tableau dashboards and reports that are reusable, trustworthy, and grounded in centralized, well‑modeled data
• Translate ambiguous business questions into clear analyses that explain what happened, why it happened, and what to do next
• Partner with business stakeholders and analytics peers to define requirements, metrics, and scalable analytics solutions
• Contribute to shared analytics standards and best practices across SQL, Dataform, Tableau, and documentation
• Leverage modern AI tools and LLM‑based coding agents to accelerate analytics workflows (e.g., SQL development, modeling, debugging, documentation), while applying strong judgment to validate correctness, performance, and data quality