Data Engineering Intern: Build Pipelines & Cloud ETL

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

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.