Data Engineering Intern

Confidential; United States; On-site; USD 28,000 - 48,000; Full time

Job summary

Confidential is seeking a Data Engineering Intern in the United States to gain hands-on experience in building data pipelines, managing data warehouses and lakes, and working with cloud platforms. You will assist in ETL/ELT processes, write SQL, and help optimize data workflows while collaborating with data analysts and scientists on real projects.

The role emphasizes learning modern data engineering tools, documenting pipelines, and contributing to scalable data architecture.

Qualifications

• Pursuing a degree in CS, Data Science, or related field with strong academics.
• Solid SQL and data manipulation skills and familiarity with relational databases.
• Basic to intermediate Python for data processing and automation.
• Understanding of ETL/ELT concepts and data pipeline architectures.

Responsibilities

• Assist in designing, developing, and maintaining data pipelines for structured and unstructured data.
• Support ETL/ELT processes to collect, transform, and load data from multiple sources.
• Develop and optimize SQL queries for data extraction, transformation, and analysis.
• Assist in building data ingestion workflows from APIs, databases, files, and other data sources.
• Monitor data pipelines and troubleshoot data processing or integration issues.
• Collaborate with data analysts and data scientists to understand data requirements.
• Document data pipelines, workflows, schemas, processes, and technical requirements.

Skills

SQL

Relational databases

Python

Data pipelines

ETL/ELT concepts

Cloud computing

Git

APIs/JSON/CSV

Tools

Apache Spark

Airflow

Kafka

Databricks

Power BI

Tableau

Job description

We are hiring a Data Engineering Intern on behalf of one of our clients in the United States. This opportunity is suitable for individuals interested in building practical experience in data engineering, data pipelines, databases, cloud technologies, and large-scale data processing.

Key Responsibilities

• Assist in designing, developing, and maintaining data pipelines for structured and unstructured data
• Support ETL and ELT processes to collect, transform, and load data from multiple sources
• Develop and optimize SQL queries for data extraction, transformation, and analysis
• Assist in building data ingestion workflows from APIs, databases, files, and other data sources
• Perform data cleaning, validation, transformation, and quality checks
• Work with relational and non-relational databases to support data storage and processing requirements
• Assist in designing and maintaining data warehouse and data lake solutions
• Support the development of automated data workflows and scheduled data processing jobs
• Monitor data pipelines and troubleshoot data processing or integration issues
• Collaborate with data analysts, data scientists, and other technical teams to understand data requirements
• Assist in improving data pipeline performance, reliability, scalability, and efficiency
• Document data pipelines, workflows, schemas, processes, and technical requirements
• Support data migration and integration activities across different platforms and systems
• Assist with implementing data validation and data quality frameworks
• Work with cloud-based data services and infrastructure as part of ongoing data engineering projects
• Participate in code reviews and follow established development and documentation practices
• Assist in identifying opportunities to automate repetitive data engineering and processing tasks
• Stay updated with modern data engineering tools, technologies, and best practices

Technical Skills

• Strong understanding of SQL and relational databases
• Basic to intermediate knowledge of Python for data processing and automation
• Understanding of ETL and ELT concepts and data pipeline architecture
• Familiarity with databases such as MySQL, PostgreSQL, SQL Server, or similar platforms
• Basic understanding of data warehousing, data lakes, and data modeling
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud is preferred
• Exposure to tools such as Apache Spark, Databricks, Airflow, Kafka, or similar technologies is an advantage
• Familiarity with Git and version control systems
• Understanding of APIs, JSON, CSV, and other common data formats
• Basic knowledge of data integration and orchestration concepts
• Familiarity with Power BI, Tableau, or other analytics tools is an advantage

Required Skills

• Strong analytical and problem-solving abilities
• Good understanding of programming and database concepts
• Attention to detail when working with large datasets
• Ability to troubleshoot technical and data-related issues
• Strong communication and collaboration skills
• Ability to work independently and contribute effectively within a technical team
• Willingness to learn new technologies and adapt to evolving data engineering environments
• Good documentation and organizational skills

Why Join Us

• Gain hands-on exposure to real-world data engineering projects
• Develop practical experience in building and managing data pipelines
• Work with modern data engineering tools, technologies, and cloud platforms
• Strengthen your SQL, Python, database, and data processing skills
• Gain exposure to ETL, ELT, data warehousing, and data integration processes
• Learn how organizations collect, process, store, and manage large volumes of data
• Gain experience collaborating with data engineers, analysts, data scientists, and other technical professionals
• Develop practical knowledge of data quality, pipeline monitoring, and workflow automation
• Work on projects that can help strengthen your technical portfolio
• Improve your understanding of scalable and reliable data architecture
• Gain exposure to industry-oriented development and documentation practices
• Strengthen your technical problem-solving and debugging abilities
• Build a foundation for future opportunities in Data Engineering, Data Platform, Cloud Engineering, or related technical fields
• Opportunity to learn and work with evolving technologies in the modern data ecosystem