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Data Engineering Internship Jobs (NOW HIRING)

Hazen and Sawyer

General Engineering Internship

Raleigh, NC · On-site

$14.75 - $19.25/hr

... internships as well Why Hazen and Sawyer: * Founded in 1951 by the son of Allen Hazen (developer of ... Manage data for process modeling and design * Coordinate with manufacturers and vendors to select ...

Octaura

Intern, Data Engineering

New York, NY

The Opportunity Octaura offers exciting summer internship opportunities, especially for those ... We are seeking motivated and enthusiastic summer interns to join our data engineering team. An ...

Jump Trading

Campus Data Engineer (Intern)

Chicago, IL · On-site

$250K/yr

Our data engineering internship is an intensive 10-week experience with our team who is responsible for developing and maintaining the infrastructure and pipelines to support data for firm wide use.

Jump Trading

Campus Data Engineer (Intern)

Chicago, IL · On-site

$250K/yr

Our data engineering internship is an intensive 10-week experience with our team who is responsible for developing and maintaining the infrastructure and pipelines to support data for firm wide use.

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How much do data engineering internship jobs pay per hour?

As of Jul 29, 2026, the average hourly pay for data engineering internship in the United States is $19.31, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.91 per hour, depending on experience, location, and employer.

What types of projects or tasks can I expect to work on during a Data Engineering Internship?

As a Data Engineering Intern, you can expect to work on a variety of tasks such as building data pipelines, cleaning and transforming raw data, and assisting in the integration of data from multiple sources. You may also support the team in optimizing database performance and ensuring data quality. Interns often collaborate with data scientists, analysts, and software engineers, gaining exposure to different tools and technologies like SQL, Python, and cloud platforms. These experiences provide a strong foundation for a future career in data engineering.

What are the key skills and qualifications needed to thrive as a Data Engineering Intern, and why are they important?

To thrive as a Data Engineering Intern, you need a solid grasp of programming (especially Python or Java), databases (SQL/NoSQL), and data structures, often demonstrated by coursework or relevant projects. Familiarity with data processing tools like Apache Spark, ETL pipelines, and cloud platforms such as AWS or Google Cloud is highly beneficial. Strong problem-solving abilities, attention to detail, and effective communication help interns excel in collaborative and fast-paced environments. These skills are crucial for building reliable data pipelines and supporting data-driven decision-making within organizations.

What is the difference between Data Engineering Internship vs Data Analyst Internship?

AspectData Engineering InternshipData Analyst Internship
Required SkillsSQL, Python, ETL, cloud platformsExcel, SQL, data visualization tools
Work EnvironmentData pipelines, backend systems, cloud infrastructureData reporting, dashboards, business insights
Industry UsageTech, finance, healthcare, e-commerceRetail, marketing, finance, consulting

Data Engineering Internships focus on building and maintaining data pipelines and infrastructure, requiring technical skills like SQL and Python. Data Analyst Internships emphasize analyzing data to generate insights, often using Excel and visualization tools. Both roles are common in tech-driven industries but serve different functions within data teams.

What is a Data Engineering Internship?

A Data Engineering Internship is a temporary position designed for students or recent graduates to gain practical experience in building, managing, and optimizing data pipelines and infrastructure. Interns typically work alongside professional data engineers to learn about data extraction, transformation, and loading (ETL) processes, database management, and data warehousing. This role provides hands-on exposure to tools like SQL, Python, and cloud platforms, helping interns develop essential technical skills for a career in data engineering. Interns may also assist in improving data quality and supporting analytics initiatives within a company.
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Data Engineering Internship jobs near you
Infographic showing various Data Engineering Internship job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.

Data Engineering Internship (Summer 2027)

Castleton Commodities International, LLC

Stamford, CT • On-site

$122K - $146K/yr

Full-time

Posted 9 days ago

Job description

Application Deadline: September 1, 11:59 pm EST
Program Summary - Data Science & Technology Internship
Company Overview:
Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets, providing the Company with market insights and access. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in, and develop, select commodity infrastructure assets. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.
Position Overview:
CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are looking for motivated and detail-oriented Data Engineering Interns to join our Global Data Science & Technology team in Houston, TX, Stamford, CT, & New York City offices. The Data Engineering Intern will work closely with our Data Science, Data Engineering and Commercial teams to build and optimize data pipelines that power our analytics, forecasting, and investment decision-making processes. This is a hands-on technical internship ideal for someone who enjoys solving real-world data challenges, especially around ingesting, scraping, and managing large datasets across the commodity markets.
Responsibilities:
  • Develop and maintain robust data ingestion pipelines from various internal and external sources, including APIs, FTP endpoints, and cloud data providers.
  • Develop data ingestion and transformation pipelines using Python and SQL, publishing Snowflake for downstream use in analytics and forecasting tools.
  • Work on data architecture and data management projects for both new and existing data sources.
  • Design and implement ETL processes to clean, normalize, and store structured and semi-structured data in Snowflake, our core relational data warehouse.
  • Analyze data pipeline performance and implement optimizations to improve efficiency and reliability.
  • Conduct data quality checks and build validation logic to identify anomalies and ensure data integrity for use by commercial trading and analytics teams.
  • Automate data workflows using Python, SQL, and orchestration tools (e.g., Airflow or similar).
  • Assist in transitioning legacy datasets and codebases into scalable, cloud-native workflows aligned with our modern data architecture.
  • Document data sources, pipeline logic, and data models to ensure maintainability and knowledge transfer.

Qualifications:
  • Currently pursuing a Bachelor's or higher degree in Computer Science, Engineering, Management Information Systems, or related technical field.
  • Expected graduation date of Winter 2027 or Spring/Summer 2028.
  • Strong programming experience in Python (preferred libraries: pandas, NumPy, SQL alchemy, etc.).
  • Strong understanding of SQL and experience querying relational databases (Snowflake a plus).
  • Exposure to or interest in cloud platforms (e.g., AWS, Azure), particularly with cloud data storage and compute.
  • Familiarity with web scraping frameworks and handling large-scale structured and unstructured data sources.

Apply