阿里巴巴阿里国际-高级数据研发工程师（电商业务ETL&建模）-杭州

任职要求

1，本科及以上学历，计算机科学、数据科学、电子商务、统计学或相关专业。
2，两年以上电商数据仓库或相关工作经验，熟悉电商业务流程，有大数据处理和ETL工具（如Hive、Spark、Flink、DataWorks等）使用经验者优先，有AI在数据处理或分析领域应用经验者更佳。
3，熟悉数据仓库建模理论（如维度建模、数据湖等），具备扎实的SQL功底，熟练掌握Hadoop、Spark等大数据处理框架，了解常用的机器学习算法和AI技术在数据处理和分析中的应用。
4，具备优秀的业务理解能力，能够深入理解电商、招商和营销等业务需求，并将其转化为高效的数据解决方案和可落地的AI应用场景。
5，具备良好的团队合作精神和沟通能力，能够与业务、算法、工程等团队有效协作，具备较强的分析问题、解决问题和创新思维能力，对新技术和AI应用在电商领域充满热情。

1，Bachelor's degree or above in Computer Science, Data Science, E-commerce, Statistics, or related fields.
2，More than two years of experience in e-commerce data warehousing or related fields, familiar with e-commerce business processes, with a preference for those with experience in big data processing and ETL tools (such as Hive, Spark, Flink, DataWorks, etc.), and even better if they have experience applying AI in data processing or analysis.
3，F…

工作职责

1，负责电商数据仓库的ETL流程设计、开发与优化，尤其关注招商、营销活动（含大促）等核心业务场景的数据集成，确保数据的准确性、实时性和为业务决策和AI应用提供高质量数据基础。
2，主导电商领域的数据建模工作，构建满足用户画像、商品分析、营销效果评估等业务需求的多维数据模型，支持精细化运营、个性化推荐和智能决策。
3，与电商业务、招商、营销业务和数据科学等组紧密合作，深入理解业务痛点和增长目标，提供创新的数据解决方案，优化数据处理流程，提升数据赋能业务的能力。
4，参与大数据平台的优化和扩展，探索并应用AI技术（如机器学习、自然语言处理等）提升数据处理效率、数据质量和数据洞察能力，例如智能数据清洗、异常检测、特征工程自动化等。
5，编写高质量的代码和技术文档，确保代码的可维护性、可扩展性和可理解性，并积极参与技术分享和知识沉淀。

1，Responsible for the design, development, and optimization of ETL processes for the e-commerce data warehouse, with a focus on core business scenarios such as merchant acquisition, marketing campaigns (including major promotions), ensuring accurate, real-time, and efficient data transmission, and providing high-quality data foundation for business decisions and AI applications.
2，Lead data modeling efforts in the e-commerce domain, building multi-dimensional data models that meet the business needs of user profiling, product analysis, marketing performance evaluation, etc., supporting refined operations, personalized recommendations, and intelligent decision-making.
3，Collaborate closely with e-commerce business, merchant acquisition, marketing, and other teams to deeply understand business pain points and growth objectives, provide innovative data solutions, optimize data processing workflows, and enhance the ability of data to empower business.
4，Participate in the optimization and expansion of big data platforms, explore and apply AI technologies (such as machine learning, natural language processing, etc.) to improve data processing efficiency, data quality, and data insight capabilities, such as intelligent data cleaning, anomaly detection, and automated feature engineering.
5，Produce high-quality code and technical documentation to ensure code maintainability, scalability, and understandability, and actively participate in technical sharing and knowledge accumulation.