携程

岗位职责：招聘对象：本、硕、博。 毕业时间：2026 年 9 月至 2027 年 8 月期间毕业（中国大陆以毕业证为准，非中国大陆地区以教育部学位认证为准）且最高学历毕业后无全职工作经验的学生。 Responsibilities: Optimize the design and implementation of scalable and efficient data architectures, such as streaming data processing, data pipelines, and distributed systems. Develop a real-time data computing and storage platform, ensuring stability and high availability of OLTP and OLAP data query and analysis services. Understand the specific reports, analyses, and insights that need to be derived from data, and create data-driven solutions to effectively support operational analytics needs. Monitor and enhance the performance of data infrastructure, including query optimization, resource management, and data partitioning strategies. Collaborate with cross-functional teams, data scientists, and data engineers to gather data requirements, and provide comprehensive documentation for data infrastructure solutions and best practices. 任职要求：Qualifications: Bachelor's or master's degree in computer science, data engineering, or a related field is required. Candidates with coursework or specialization in database systems, distributed computing, and cloud technologies are preferred. Proficiency in programming languages such as Java, C++, or Go, as well as hands-on experience with both SQL and NoSQL database systems, is required. Familiarity with distributed computing frameworks, such as Apache Flink, Apache Spark, Apache Hadoop, and Presto, is a plus. Experience working with cloud platforms, such as AWS, Aliyun, or GCP, is also preferred. Proficiency in using Git version control tools is required. Prior experience with distributed databases, NoSQL, message queues, caching, and TCP/IP principles, with the ability to design complex systems for high concurrency and large data volumes. A strong sense of ownership, a collaborative mindset, and excellent analytical and communication skills. Experience with AI-assisted development tools (e.g., Claude Code, Codex, GitHub Copilot) to improve coding, debugging, and data pipeline development efficiency is a plus; familiarity with LLM-powered application scenarios (e.g., RAG, AI-driven data processing) is preferred.