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Data Engineer Intern (Hybrid) - June 2026

Staples • Full-time • Framingham, MA, US • 3d ago

Staples is business to business. You’re what binds us together.

We’re searching for you: fresh talent with new ideas, innovation, passion, and drive to bring to Staples. We have student and graduate opportunities where you’ll help the Staples, Inc. organization be more successful and productive while building a purposeful career. Whether you are looking for a summer internship, co-op, or an entry-level role, we’ll help you develop the technical and soft skills you need to thrive in the future.

This intern position requires you to be in a hybrid working model (at least 3 days in the office, and 2 days remote) based at the Staples, Inc. Corporate Headquarters in Framingham, MA.

Target Start Date: June 1, 2026 - August 14, 2026 (11-week program)

What you bring to the table:

Collaborative – able to build partnerships and work collaboratively with others to meet shared objectives
Customer Focused – able to identify and understand internal or external customer needs and interests and deliver customer-centric solutions
Inclusive – dedicated to fostering an inclusive environment consisting of diverse individuals from varying backgrounds and cultures
Innovative – develop new insights; question conventional approaches; encourage new ideas; design and implement new solutions
Self-Developer – actively seeks new ways to grow and be challenged using both formal and informal development channels

What you’ll be doing:

Build & Optimize Pipelines – Assist in developing ETL/ELT workflows using Azure Data Factory, Python, SQL, and PySpark.
Data Transformation – Implement business logic and data modeling within Databricks and Delta Lake.
Workflow Orchestration – Work with tools such as Airflow or Tidal to schedule, monitor, and troubleshoot jobs.
SQL Development – Write, debug, and optimize queries for reporting, validation, and transformation tasks.
Automation & Validation – Contribute to Python/PySpark scripts for ETL, data validation, and automation.
AI/ML Data Prep – Prepare datasets for ML models (feature extraction, cleansing, transformation).
Model Integration – Support experiments by integrating ML model outputs into pipelines and dashboards.
Documentation & Quality – Participate in validation, reconciliation, and documentation to ensure accuracy and consistency.

What’s needed - Basic Qualifications:

Pursuing a master’s degree, enrolled as a full-time student carrying a minimum of 12 college credits
Strong foundation in SQL and Python programming.
Good understanding of data engineering concepts
Understanding and Interest in AI/ML and applying data to solve real business problems
Strong analytical, problem-solving, and communication skills
Team-oriented, eager to learn, and adaptable

Preferred Qualifications: