Data Engineer – Point Card System Department, Rakuten Payment, Inc.
NBF Shinagawa Tower, Japan
Rakuten
楽天グループ株式会社のコーポレートサイトです。企業情報や投資家情報、プレスリリース、サステナビリティ情報、採用情報などを掲載しています。楽天グループは、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ことを目指しています。Job Description:
Designs, builds and oversees the deployment and operation of technology architecture, solutions and software to capture, manage, store and utilize structured and unstructured data from internal and external sources. Establishes and builds processes and structures based on business and technical requirements to channel data from multiple inputs, route appropriately and store using any combination of distributed (cloud) structures, local databases, and other applicable storage forms as required. Develops technical tools and programming that leverage artificial intelligence, machine learning and big-data techniques to cleanse, organize and transform data and to maintain, defend and update data structures and integrity on an automated basis. Creates and establishes design standards and assurance processes for software, systems and applications development to ensure compatibility and operability of data connections, flows and storage requirements. Reviews internal and external business and product requirements for data operations and activity and suggests changes and upgrades to systems and storage to accommodate ongoing needs. May be internal or external, client-focused, working in conjunction with Professional Services and outsourcing functions. May include company-wide, web-enabled solutions.
Collaboration & Interaction: Networks with key contacts outside own area of expertise. Adapts style and uses persuasion in delivering messages that relate to the wider firm business. Frequently advises others on complex matters. May be accountable through team for delivery of tactical business targets.
Complexity & Problem Solving: Understands and works on complex issues where analysis of situations or data requires an in-depth evaluation of variable factors. Determines methods and procedures on new assignments. Exercises judgment in selecting methods, evaluating, adapting of complex techniques and evaluation criteria for obtaining results. Work is reviewed upon completion and is consistent with departmental objectives.
Knowledge & Application: Exhibits advanced wide-ranging experience, using in-depth professional knowledge, acumen, concepts and company objectives to develop, resolve complex models and procedures. Provides solutions to issues in creative and effective ways. Understands the interrelationships of different disciplines. Directs the application of existing principles and guides development of new policies and ideas.
Typical Degree & Years of Experience: Typically requires a minimum of 8 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or a PhD with 3 years experience; or equivalent experience. Some barriers to entry exist at this level, requiring department review.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Architecture DataOps Machine Learning PhD Unstructured data
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