-数据仓库工程师-Data Warehouse Engineer

职位描述

l 参与离线和实时数据仓库架构设计和开发，构建高效、稳定、可扩展的数据仓库系统。

l 负责数据仓库模型设计，包括星型模型、雪花模型、星座模型等，并制定数据仓库开发规范。

l 负责数据资产的运营，结合业务现状，解决数据资产生产、消费过程中的卡点，提升相关团队的用数体验

l 开发和维护 ETL/ELT 数据 pipeline，确保数据高效、准确地从源系统加载到数据仓库

l 优化数据仓库性能，解决数据查询和数据加载的性能瓶颈。

任职资格

l 计算机科学、软件工程、信息管理或相关专业本科及以上学历。

l 5年以上数据仓库相关项目经验，熟悉数据仓库建设流程，对数据治理、数据资产运营都体系化落地经验

l 精通 SQL 语言，熟悉大数据体系，包括但不限于Spark、Hive、Flink、Paimon等。

l 熟悉并了解主流的数仓理论，包括Kimball建模、指标体系理论

l 具备良好的沟通能力，能够清晰地向非技术人员解释数据领域概念。

l 具备良好的团队合作精神，能够与不同部门紧密合作。

加分项

l 有PB级数仓构建经验的优先，有团队管理经验的优先

l 具备国际化经验或背景，熟悉不同国家和地区的市场环境和商业文化的优先。

Job Description

• Data Warehouse Architecture & Development: Participate in the architecture design and development of offline and real-time data warehouses, building high-efficiency, stable, and scalable data warehouse systems.
• Data Modeling & Standards: Responsible for data warehouse model design (including star schema, snowflake schema, constellation schema, etc.) and establishing data warehousing development standards.
• Data Asset Operations: Drive data asset operations by aligning with current business needs, removing bottlenecks across data production and consumption, and enhancing the overall data usage experience for relevant teams.
• Pipeline Engineering: Develop and maintain ETL/ELT data pipelines to ensure accurate and efficient data extraction and loading from source systems into the data warehouse.
• Performance Tuning: Optimize data warehouse performance and resolve bottlenecks in data query execution and data loading process.

Qualifications

• Education & Experience: Bachelor’s degree or above in Computer Science, Software Engineering, Information Management, or related fields; 5+ years of experience in data warehousing projects with a deep understanding of data warehouse construction methodologies and hands-on, systematic experience in data governance and data asset operations.
• Big Data Stack: Proficient in SQL and fluent in the big data ecosystem, including but not limited to Spark, Hive, Flink, Paimon, etc.
• Warehousing Theory: Solid understanding of mainstream data warehousing methodologies, including Kimball modeling and key metric framework theories.
• Communication Skills: Excellent communication skills, with the ability to explain complex data concepts clearly to non-technical stakeholders.
• Collaboration: Strong team player capable of working closely with cross-functional departments.

Preferred Qualifications / Nice-to-Haves

• Scale & Leadership: Experience in building PB-scale data warehouses is preferred; team management experience is a plus.
• Global Background: Experience or background in international business, with familiarity in market environments and business cultures across different regions, is preferred.