Data Engineer Intern

- Bachelor's degree or above in a quantitative/technical field such as computer science, engineering, statistics
- Speak, write, and read fluently in Mandarin
- Proficient in writing performant SQL working with large data volumes
- Comfortable with scripting languages i.e. Python, Perl, etc
- Experience with large scale data processing, data structure optimization and scalability of algorithms a plus
- Master's degree in a quantitative/technical field such as computer science, engineering, statistics
- Excellent problem solving skills.
- Candidate must have good written and oral communication skills, be a fast learner and have the ability to adapt quickly to a fast-paced development environment.

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请注意，申请此实习岗位需要满足如下条件：
毕业时间：25年底至26年9月之间毕业
面试时间：25年1月开始
入职日期：25年4月-6月
实习时间：一周实习5天，至少持续6个月
工作地点：北京市
Do you want to be in the forefront of engineering big data solutions that takes Transportation models to the next generation? Do you have a solid analytical thinking, metrics driven decision making and want to solve problems with solutions that will meet the growing worldwide need? We are looking for top notch Data Engineering Interns to be part of our world class Transportation Business Intelligence team. We are building real time analytical platforms using big data tools and AWS technologies.
The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. He/she is a self-starter, comfortable with ambiguity, able to think big (while paying careful attention to detail), and enjoys working in a fast-paced and global team. It's a big ask, and we're excited to talk to those up to the challenge!

- Currently enrolled from an accredited college or university, actively pursuing a degree in one of the following areas: Statistics or Computer Science


- Proficient in writing performant SQL working with large data volumes

- Comfortable with scripting languages i.e. Python, Perl, etc

- Experience with large scale data processing, data structure optimization and scalability of algorithms a plus