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Data Engineer - Data Integration Intern

Yesterday 2026/06/03
500 Employees or more · IT Services
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Job description

Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.





Your role and responsibilities

Data Engineer - Data Integration Intern, you will support the design, development, and maintenance of data pipelines that integrate data from multiple sources into centralized data platforms. You will work closely with data engineers, analysts, and business teams to ensure data is reliable, accessible, and ready for analysis





Required education
Bachelor's Degree

Preferred education
Bachelor's Degree

Required technical and professional expertise

* Basic understanding of data engineering concepts (ETL/ELT, data pipelines, data integration).
* Knowledge of SQL and relational databases.
* Familiarity with at least one programming language such as Python or Java.
* Understanding of data formats (CSV, JSON, Parquet is a plus).
* Basic knowledge of version control systems (e.g., Git).
* Strong analytical and problem-solving skills.
* Willingness to learn and ability to work in a team environment.





Preferred technical and professional experience

* Exposure to data integration or ETL tools (e.g., Airflow, Talend, Informatica, dbt).
* Familiarity with cloud platforms (AWS, Azure, or GCP).
* Basic understanding of data warehousing concepts (fact tables, dimensions, schemas).
* Experience with APIs and data extraction from external systems.
* Awareness of data quality, data validation, and logging practices.








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