Businessに必要なData Pipeline / Applicationを構築し、それらを運用するBusiness Intelligence Engineerを募集します。
Eコマースモールを運営しています。「地球上で最もお客様を大切にする企業になること」を企業理念に掲げ、お客様が欲しい商品を欲しい時に最速のお届けでお買い物出来るようなネットワークの構築を行っています。遅延なく商品を入荷しお客様に幅広いセレクションを提供し、お約束通りに商品をお届けすることはサービスの根幹ですが、物流・小売業界を取り巻く環境は日々変化しています。常に変化する状況にあってもお客様のために常に高いサービス品質を提供することは、当社の競争力に必要不可欠な要素です。
Amazon Japanの成長に伴って拡大を続けるサプライチェーンは日々複雑性を増しており、トランスポーテーションBusinessもそれに伴い複雑化しています。日々のビジネスを運用していくには、トランスポーテーションに関する巨大なData Pileline・Data Mart・指標・ダッシュボードの作成や改善、Operationの安定稼働や効率化が欠かせません。さらには、機械学習をはじめとした技術を通じて、これらのBusiness Operation自体の効率化が求められています。AWSやAmazon 独自のToolを使ってこれらのチャレンジに対して最適なソリューションを提供するBusiness Intelligence Engineerを募集しています。
The Amazon Japan Transportation is looking for Business Intelligence Engineer to build the Data Pipelines / Applications necessary for business and operate them.
We operate one of the largest e-commerce businesses in Japan. We strive to be” Earth’s most customer-centric company” and we are building a fulfillment network which customers can shop with the fastest delivery when they want any goods they want. It is our foundation to provide customers with a wide selection and deliver products as promised, but the environment around the logistics and retail industry is changing day by day. Our supply chain logistics is becoming more complex every day under continuous growth of Amazon Japan and transportation businesses have also become more complex along with it. Therefore, there is a need to create and improve indicators and dashboards related to transportation, stable operation and efficiency of operations, and even to improve the efficiency of these business operations themselves, and we are looking for Business Intelligence Engineer that provide optimal solutions to these challenges using AWS and Amazon internal tools
Key job responsibilities
- Pipeline(ETL)・テーブルやデータマート・ダッシュボードを構築し、安定的に稼働するように改良を行います。
- AWSやAmazon Internal Toolを使ったアーキテクチャの設計から開発、運用までを担います。特に、機械学習や組み合わせ最適化・確率統計等のアルゴリズムのニーズが増しています。
- pipelineが安定的に実行されるよう監視・リソース管理します。また、安定稼働を支えるインフラ監視・管理の自動化なども行います。
- Regionとも積極的に連携し、他Regionで導入している Solutionが日本でも有効な場合は、導入計画から実行までを行います。
- Amazon内で導入されているAI / LLM を理解し、各ビジネスユーザが使用するよう啓蒙し、また、必要に応じてトレーニングやユースケースのシェアなどをおこないます。
- Build and maintain reports / dashboards for customer to track performance and provide insights. Collaborate with internal stakeholders across individual teams or multiple teams , define metrics and develop them. Also, when creating reports / dashboards, tables, data marts, and various pipelines (ETL) are created as needed, and these are built to be executed automatically.
- In the business processes carried out by each transportation team, we understand each team's pain point and provide solutions. In providing solutions, we are responsible for end to end solution from architecture design using AWS / Amazon internal tool, development, and operation. Also, when machine learning is necessary for creating forecasts, etc., we will even implement it using an appropriate model for the requirements.
- Monitor pipelines that run on a daily basis to ensure stable execution. If events such as Prime Day or Black Friday are expected to be tight ahead of time, we will consider measures such as expanding in advance.
- Actively cooperate with regions other than Japan, and if solutions introduced in other regions are effective in Japan, Will carry out from implementation planning to execution.
- Understand AI / LLM implemented in Amazon, educate each business user to use it, and conduct training and / or share use cases as needed.
A day in the life
10 : 00 出勤。自分がLeadしているプロジェクトのコードを書く。10 : 30 同僚が実装した新しいデータパイプライン安定化ツールの説明を受ける。前から欲しかった機能がついており感動。11 : 00 SQLで新しいパイプラインを実装12 : 00 ランチ。今週の週替わりメニューのクスクスを初めて食べてみる。エキゾチック!13 : 30 Scrum Daily Meeting。同僚の進捗を確認しつつ情報交換。Metricsの設計に別プロジェクトの知見が使えそう14 : 00 Biz sideとフラッシュディスカッション。指標のユースケースについて教えてもらい、実装アイデアについて目線合わせ15 : 00 一旦退社。残りは家で18 : 00 明日のProject Weeklyの準備。進捗をNotebookに保存。19 : 30 お仕事終了
10 : 00 Arrived at work. Wrote code for the project I am leading.10 : 30 Received an explanation from a colleague about a new data pipeline stabilization tool they implemented. I was excited to see the new feature I had wanted.11 : 00 Implemented a new pipeline using SQL12 : 00 Lunch. Tried the weekly special menu item, couscous, for the first time. It was exotic!13 : 30 Scrum Daily Meeting. Checked on my colleagues' progress while exchanging information. The design of the metrics could use insights from another project.14 : 00 Flash discussion with the business side. Learned about the use cases for the metrics, and aligned on implementation ideas.15 : 00 Left the office for the day. The rest will be done from home.18 : 00 Prepared for tomorrow's Project Weekly. Saved the progress in a Notebook.19 : 30 Finished work for the day.About the team
SCM)・ミドルマイル
Business Intelligence Engineer
BASIC QUALIFICATIONS
3+ years experience of complex Data Analysis, under the either environment. 1 : SQL / 2 : Python, Numpy, PandasExperience with data visualization using either tech stack. 1 : Tableau, Quicksight / 2 : Matplotlib or similar toolsExperience with data modeling, warehousing and building ETL pipelinesBasic understanding of at least one Script computer language, such as Python.Bachelor's degreeBusiness-level Japanese proficiencyBusiness-level English proficiencyPREFERRED QUALIFICATIONS
Experience of building machine learning solutions that have had a business impact, such as GBDT, DNN, BERTExperience with AWS solutions such as EC2, DynamoDB, S3, and RedshiftExperience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modelingExperience applying basic statistical methods (e.g. regression) to business problemsOur inclusive culture empowers Amazonians to deliver the best results for our customers.