We are looking for a motivated and detail-oriented Data Engineering Intern to join our team. The candidate will work closely with experienced data engineers to support the development, maintenance, and optimization of data pipelines and data processing systems. This internship provides hands-on experience in data integration, database management, and data engineering best practices.

Key Responsibilities:

• Assist in designing, developing, and maintaining data pipelines and ETL/ELT workflows.
• Collect, clean, transform, and process structured and unstructured data from multiple sources.
• Write and optimize SQL queries for data extraction and analysis.
• Work with databases and data storage systems to ensure data accuracy and consistency.
• Assist in building automated workflows for data ingestion and processing.
• Support data quality checks, validation, and troubleshooting.
• Collaborate with data engineers, analysts, and other technical team members.
• Document data workflows, processes, and technical solutions.
• Learn and implement best practices for data security, reliability, and performance.

Required Skills and Qualifications

• Currently pursuing or recently completed a degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
• Basic knowledge of SQL and relational databases.
• Familiarity with Python programming.
• Understanding of database concepts, data structures, and data processing fundamentals.
• Basic understanding of ETL/ELT processes and data pipelines.
• Analytical thinking, problem-solving skills, and attention to detail.
• Good communication skills and willingness to learn.

Preferred Skills

• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Exposure to tools such as Apache Spark, Airflow, or Kafka.
• Basic knowledge of data warehouses and platforms such as Snowflake or BigQuery.
• Experience with Git and version control.
• Academic projects or personal projects involving databases, Python, or data processing.