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How much do merchandising internship summer jobs pay per hour?

As of Aug 4, 2026, the average hourly pay for merchandising internship summer in the United States is $17.44, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $19.23 per hour, depending on experience, location, and employer.

What does a merchandising internship summer do?

As a Merchandising Intern during the summer, your daily responsibilities often include assisting the merchandising team with inventory analysis, tracking product performance, and supporting the execution of in-store and online promotions. You may also participate in cross-functional meetings with buying, marketing, or planning teams, and help prepare sales reports or presentation materials. Depending on the company, interns may have opportunities to visit retail locations or engage in special projects. This hands-on experience is designed to give you a comprehensive understanding of merchandising operations and help build valuable skills for a future career in retail or merchandising.

What is a merchandising internship summer?

A Merchandising Internship Summer job is a temporary position designed for students or recent graduates to gain hands-on experience in retail merchandising. Interns typically assist with product assortment, inventory management, sales analysis, and visual merchandising. They may work closely with buyers, planners, and store teams to understand consumer trends and optimize product placement. This internship helps develop skills in data analysis, market research, and communication while providing exposure to the retail industry.

What are the key skills and qualifications needed to thrive in a merchandising internship summer?

To thrive as a Merchandising Internship Summer, you need a strong analytical mindset, attention to detail, and a background in business, marketing, or retail-related studies. Familiarity with Microsoft Excel, retail inventory systems, and data visualization tools is often beneficial. Strong communication, teamwork, and organizational skills will help you stand out in a fast-paced retail environment. These abilities are vital for supporting merchandising decisions, optimizing product assortments, and contributing effectively to team projects.

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Merchandising Internship Summer jobs near you
Infographic showing various Merchandising Internship Summer job openings in the United States as of July 2026, with employment types broken down into 3% Internship, 71% Full Time, 24% Part Time, 1% Temporary, and 1% Contract. Highlights an 83% Physical, 1% Hybrid, and 16% Remote job distribution, with an average salary of $36,285 per year, or $17.4 per hour.

Data Engineering Internship (Summer 2027)

Castleton Commodities International LLC

Stamford, CT

$122K - $146K/yr

Full-time

Posted 14 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