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Data Engineering Internship

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Onsite

Data Engineering Internship

Springer Capital

Data Engineering Internship

Skills

Google Cloud PlatformSQL QueriesBig DataData EngineeringPythonJavaKafkaData AnalysisScalaAWSHadoopAzure

1 day ago

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Important information


Contract type:

Internship

Location:

Nigeria

Work mode:

Onsite

Published on:

22 October 2026

What they need


Job summary

We are seeking motivated Data Engineering Interns to join our team remotely for a 3-month internship. This role is designed for students or recent graduates interested in working with data pipelines, ETL processes, and big data tools. You will gain practical experience in building scalable data solutions. While this is an unpaid

Min Qualification: High School (S.S.C.E) Experience Level: No Experience Experience Length: No Experience/Less than 1 year Language Requirement: English Work Type: Internship & Volunteer Working Hours: Flexible Hours Applicant Location: Remote

Job descriptions & requirements

Responsibilities:

  • Assist in designing and building data pipelines for structured and unstructured data.
  • Support ETL (Extract, Transform, Load) processes to prepare data for analytics.
  • Work with databases (SQL/NoSQL) for data storage and retrieval.
  • Help optimize data workflows for performance and scalability.
  • Collaborate with data scientists and analysts to ensure data quality and consistency.
  • Document workflows, schemas, and technical processes.

Requirements:

  • Strong interest in data engineering, databases, and big data systems.
  • Basic knowledge of SQL and relational database concepts.
  • Familiarity with Python, Java, or Scala for data processing.
  • Understanding of ETL concepts and data pipelines.
  • Exposure to cloud platforms (AWS, Azure, or GCP) is a plus.
  • Familiarity with big data frameworks (Hadoop, Spark, Kafka) is an advantage.
  • Good problem-solving skills and ability to work independently in a remote setup.

What You’ll Gain:

  • Hands-on experience in data engineering and ETL pipelines.
  • Exposure to real-world data workflows.
  • Mentorship and guidance from experienced engineers.
  • Completion Certificate upon successful completion.
  • Letter of Recommendation based on performance.

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