Data Engineer Intern

About the Role

NexGen Tech Solutions is seeking an enthusiastic and technically curious Data Engineer Intern to join their Data Engineering team. This internship offers hands-on exposure to real-world data engineering projects, data integration, cloud technologies, and big data processing frameworks.

Key Responsibilities

• Assist in designing, developing, testing, and maintaining data pipelines for structured and unstructured data.
• Support data extraction, transformation, and loading (ETL/ELT) processes from multiple data sources.
• Write and optimize SQL queries for data extraction, transformation, and analysis.
• Develop Python scripts for data processing, automation, and data integration.
• Assist with data cleaning, validation, transformation, and quality checks.
• Work with relational databases and understand data modeling concepts.
• Support the integration of data from APIs, databases, flat files, and cloud storage systems.
• Gain exposure to cloud-based data engineering services on Azure, AWS, or Google Cloud.
• Assist in troubleshooting data pipeline failures and resolving data quality issues.
• Support data warehouse and data lake development activities.
• Collaborate with Data Engineers, Analysts, and cross-functional teams to understand business and technical requirements.
• Prepare technical documentation for data pipelines, workflows, and development activities.
• Follow coding standards, version control practices, and data security guidelines.

Requirements & Tech Stack

• Good understanding of Python programming.
• Familiarity with Python libraries such as Pandas and NumPy.
• Knowledge of programming fundamentals, data structures, and basic algorithms.
• Strong foundational knowledge of SQL.
• Understanding of joins, aggregations, subqueries, and basic query optimization.
• Familiarity with relational database systems such as MySQL, PostgreSQL, or SQL Server.
• Basic understanding of database design and normalization.
• Understanding of ETL and ELT concepts.
• Basic knowledge of data pipelines and data integration.
• Familiarity with structured and semi-structured data formats such as CSV, JSON, and Parquet.
• Understanding of data cleaning, transformation, and validation techniques.
• Basic knowledge of Git and version control.
• Familiarity with Jupyter Notebook or Visual Studio Code.
• Understanding of REST APIs and data extraction methods.

Good to Have Skills

• Exposure to cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
• Basic familiarity with Azure Data Factory (ADF), Azure Databricks, or Microsoft Fabric.
• Understanding of Apache Spark or PySpark.
• Awareness of data warehousing concepts and platforms such as Snowflake, Amazon Redshift, or Google BigQuery.
• Familiarity with workflow orchestration tools such as Apache Airflow.
• Basic understanding of data lakes, lakehouse architecture, and Delta Lake.
• Exposure to Power BI or other business intelligence tools.
• Basic knowledge of Linux commands and shell scripting.
• Understanding of data modeling concepts, including star and snowflake schemas.
• Familiarity with Docker and CI/CD concepts.

Qualifications

• Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, or a related field.
• Strong analytical, logical reasoning, and problem-solving skills.
• Basic understanding of software development practices.
• Good written and verbal communication skills.
• Willingness to learn new technologies and work in a collaborative engineering environment.

What You Will Learn

• Develop and maintain production-oriented data pipelines under engineering supervision.
• Gain hands-on experience with Python, SQL, and modern data engineering tools.
• Understand cloud-based data architectures and ETL/ELT workflows.
• Learn how data warehouses, data lakes, and lakehouse platforms operate.
• Work with real-world datasets and business use cases.
• Understand data quality, pipeline monitoring, and performance optimization.
• Gain exposure to enterprise data engineering best practices.