数据开发工程师 - 大数据平台/ Data Development Engineer – Big Data Platform

负责大数据应用平台开发，持续优化平台架构与数据处理能力，支撑平台智能化、可视化能力升级；

负责海量业务数据的清洗、转换、特征提取与知识库构建等数据治理工作，提升数据质量与可用性；

负责大数据与AI方向的技术验证、功能开发与原型落地，输出合理的技术优化与迭代方案；

负责关键技术瓶颈突破和线上问题排查，推进项目落地与业务问题解决

本科及以上学历，计算机相关专业，具备3年及以上大数据开发实战经验；

具备扎实的Java、Python编程功底，熟悉数据库开发与Web前后端开发；

熟练掌握Hadoop、Spark、Hive、Flink等主流大数据生态组件，具备实时、离线数据开发经验；

熟悉AI应用开发流程，掌握RAG技术栈，了解LangChain/LangGraph框架并能进行AI功能开发与落地；

具备独立需求分析、技术选型与方案设计能力，可输出高可用、高可扩展的大数据平台解决方案；

具备良好的线上问题排查和集群应急处置能力，能够承受业务迭代压力；

熟练使用AI编程工具辅助开发，具备高效编码能力；自驱力强，学习能力优秀，沟通顺畅。 加分项： · 有大数据场景下大模型Agent设计、开发及项目落地经验；

掌握机器学习算法，并有相关业务落地项目经验。 Role Overview: Meitu's big data application platform is core to how the company turns product and business data into decision-making and visualization capabilities across a global portfolio that includes BeautyPlus, AirBrush, and MagicPic. As a Data Development Engineer, you will help evolve the platform's architecture, strengthen data governance, and bring AI capabilities into the data stack — working with high-volume data that has direct business impact. Key Responsibilities: · Lead the development of the big data application platform, continuously improving its architecture and data processing capability to support the platform's intelligence and visualization upgrades;

Own data governance for high-volume business data — including cleaning, transformation, feature extraction, and knowledge base construction — to raise data quality and usability;

Drive technical validation, feature development, and prototype delivery for Big Data + AI initiatives, producing clear technical optimization and iteration plans;

Tackle key technical bottlenecks and resolve online production issues, advancing project delivery and addressing real business problems. What You Bring: · Bachelor's degree or above in computer science or a related field, with at least 3 years of hands-on big data development experience;

Production-quality coding skills in Java and Python, and comfort working with databases and web front-end/back-end development;

Proficiency with mainstream big data ecosystem components including Hadoop, Spark, Hive, and Flink, with practical experience in both real-time and offline data development;

Familiarity with the AI application development lifecycle, a working command of the RAG stack and frameworks such as LangChain and LangGraph, and the ability to build and ship AI features end-to-end;

Ability to independently analyze requirements, evaluate and select technologies, and deliver high-availability, highly scalable big data platform solutions;

A track record of troubleshooting online production issues and handling cluster emergency response, able to deliver consistently under fast-moving business iterations;

Proficient use of AI coding tools to accelerate development; you work autonomously, pick up new frameworks quickly, and communicate technical decisions clearly to technical and non-technical stakeholders. Preferred Qualifications: · Experience designing, developing, and shipping large language model agents within big data environments;

Working knowledge of machine learning algorithms, with prior delivery of ML solutions in business contexts.