Sr. Business Intelligence Engineer, Amazon Business Japan

Amazonビジネスは、いつものAmazon（個人向けのAmazon.co.jp）に法人・個人事業主様向けの機能を追加した、ビジネス購買専門サービスです。個人向けのAmazon.co.jpの豊富な品揃えに加えビジネス向けの品揃えを拡充しています。また、法人価格や請求書払いなどの法人・個人事業主様のニーズに対応したサービスを提供しています。『地球上で最もお客様を大切にする企業であること』—これは世界で共通するAmazonの企業理念ですが、Amazonビジネスはまさに「お客様の声」から生まれました。Amazonビジネスは2015年4月に米国でスタートし、日本では2017年9月に始まりました。日本の法人・個人事業主様の購買の利便性を高めるため、当初から日本の商習慣に合わせた機能を提供しています。東証プライム市場に上場する企業の70%以上が登録しており、グローバルで連携を図りながら急速に成長を続けています。
Amazon Business is a B2B procurement service that adds business-specific features to the familiar Amazon.co.jp experience. In addition to Amazon's vast consumer selection, it offers expanded business-relevant products, corporate pricing, Pay by Invoice, and other services tailored to the needs of businesses and sole proprietors. "To be Earth's most customer-centric company" — this is Amazon's global mission, and Amazon Business was born directly from customer feedback. Launched in the US in April 2015 and in Japan in September 2017, the service has been designed from the start to align with Japanese business customs. Over 70% of companies listed on the Tokyo Stock Exchange Prime Market are registered, and the business continues to grow rapidly with global collaboration across US, EU, and other regions.
Key job responsibilities
1) Stakeholderと連携したBI Solutionの設計と実行
- Sales・PM等のStakeholderと連携し、ビジネス課題を深く理解した上で最適なBI Solutionを設計・実装
- Self-serviceで活用可能なDashboard・Reportを設計・開発し、Dataに基づく意思決定を支援
- Engineering Excellence（正確性・効率性）とOperational Excellence（品質・信頼性）の基準を確立・維持
2) 大規模データの分析による戦略的洞察の提供
- 大規模データセットを統合・分析し、トレンドの発見や成長機会の特定
- 統計的手法やData Miningを用いて、複雑なビジネス課題に対する実用的な洞察を導出
- 分析結果をSenior Managementに対して明確に伝え、戦略的な意思決定とアクションを促進
3) GenAI・自動化を活用した生産性向上
- 反復的な手作業を特定し、Python等のスクリプトやGenAIを組み合わせた自動化ソリューションを設計・実装
- 新たな技術を評価・導入し、チーム全体の生産性と分析品質を継続的に改善
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1) Design and deliver BI solutions in partnership with stakeholders:
- Partner with stakeholders to deeply understand business challenges and design optimal BI solutions
- Design and develop self-service dashboards and reports that enable data-driven decision-making
- Establish and maintain standards for engineering excellence (correctness, efficiency) and operational excellence (quality, reliability)
2) Deliver strategic insights through large-scale data analysis:
- Integrate and analyze large-scale datasets to uncover trends and identify growth opportunities
- Apply statistical methods and data mining techniques to derive actionable insights for complex business problems
- Communicate findings clearly to senior management, driving strategic decisions and actions
3) Improve productivity through GenAI and automation:
- Identify repetitive manual tasks and design automation solutions combining scripting (Python) with GenAI capabilities
- Evaluate and adopt new technologies to continuously improve team productivity and analytical quality

Basic Qualifications

- 5+ years of SQL experience
- Experience with AWS technologies
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience programming to extract, transform and clean large (multi-TB) data sets
- Experience in scripting for automation (e.g. Python) and advanced SQL skills.
- Experience leveraging GenAI/LLM tools (e.g., Amazon Q, GitHub Copilot, Claude, ChatGPT) to improve productivity in data analysis, coding, or reporting workflows
- Business level Japanese both in verbal and written

Preferred Qualifications

- Experience working directly with business stakeholders to translate between data and business needs
- Experience managing, analyzing and communicating results to senior leadership
- BS degree in a quantitative/technical field such as computer science, engineering, statistics
- Experience designing and implementing GenAI-powered solutions (e.g., RAG-based applications, AI agents, automated insight generation, LLM-integrated data pipelines) that drive measurable business impact
- Experience leading GenAI/AI adoption initiatives within an organization, including use case identification, PoC development, guardrail design, and scaling best practices
- Experience with semantic layers, MCP (Model Context Protocol), or similar frameworks that enable AI agent access to structured data
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