Data Engineer Intern
你希望在技术领域建立职业生涯，解决有意义的挑战，并成为一个致力于培养人才的公司的一部分，我们邀请你申请 KIS 实习计划。该轨道是为那些想了解数据如何支持商业决策和使组织更有效地运作的人设计的。它非常适合那些喜欢与数据合作，解决分析问题，构建可靠的数据解决方案，将信息转化为宝贵的商业见解的人。作为数据工程内部人员，你将有机会发展实际技能：· 数据库建模和优化 ·数据分析和整合 · 数据质量和数据支持 · 在整个课程中，你将与经验丰富的专业人员一起工作，接触到现实世界的项目，并加强技术和职业技能。要求 ·居住在拉丁美洲。· 熟练英语能力。· 至少16岁。·可于2026年9月开始实习计划。·可供每周30~40小时为该课程投入。·有兴趣通过实际实习机会获得专业经验 （本课程不提供学术资格或大学实习协议）. · 具有数据概念，电子表格，数据库，编程逻辑和分析解决问题的先前知识。· 目前正在追求或完成信息技术，计算机科学，软件工程，信息系统，数据科学，工程或相关领域的研究。最初发布在喜马拉雅山脉
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This is a remote position.
If you want to build a career in technology, tackle meaningful challenges, and be part of a company committed to developing talent, we invite you to apply for the KIS Internship Program. This track is designed for individuals who want to understand how data supports business decisions and enables organizations to operate more effectively. It is ideal for those who enjoy working with data, solving analytical problems, building reliable data solutions, and transforming information into valuable business insights.
What you'll doAs a Data Engineering Intern, you will have the opportunity to develop practical skills in:
· Database modeling and optimization
- Data analysis and consolidation
- Data quality and data support
- Throughout the program, you will work alongside experienced professionals, gain exposure to real-world projects, and strengthen both your technical and professional skills.
Requirements
- Reside in Latin America.
- Advanced English proficiency.
- Be at least 16 years old.
- Be available to start the internship program in September 2026.
- Be available to dedicate 30–40 hours per week to the program.
- Be interested in gaining professional experience through a practical internship opportunity (the program does not provide academic credits or university internship agreements).
- Have prior knowledge of data concepts, spreadsheets, databases, programming logic, and analytical problem-solving.
- Be currently pursuing or have completed studies in Information Technology, Computer Science, Software Engineering, Information Systems, Data Science, Engineering or related fields.
Originally posted on Himalayas