Data Engineer Intern

Global Technology Solutions (GTS) at Res Med is a division dedicated to creating innovative, scalable, and secure platforms and services for patients, providers, and people across Res Med. The primary goal of GTS is to accelerate well‑being and growth by transforming the core, enabling patient, people, and partner outcomes, and building future‑ready operations.

The strategy of GTS focuses on aligning goals and promoting collaboration across all organizational areas. This includes fostering shared ownership, developing flexible platforms that can easily scale to meet global demands, and implementing global standards for key processes to ensure efficiency and consistency.

As an intern at Res Med, you’ll gain hands‑on experience, learn from experts, and collaborate with teams that do truly amazing things.

Over the course of our 12‑week internship experience you will have the opportunity to:

• Work directly with a mentor in your field of study while finding solutions to real‑world problems
• Experience our products up close by working with one of our Digital Health Technologies teams
• Interact directly with our company leaders
• Build a lasting professional network through social events and engaging activities
• Gain the skills and knowledge to become a future leader in the medical technology industry

We are looking for a Data Platform Engineer Intern to join Res Med’s data platform team and gain hands‑on experience building and supporting modern, cloud‑based data systems. This internship is designed for students or early‑career engineers who are eager to learn how data platforms are built in production and want exposure to real‑world analytics, data engineering, and cloud technologies.

As an intern, you’ll work alongside experienced engineers who will mentor you, provide guidance, and help you grow your technical skills while contributing to meaningful projects that support Res Med’s global data ecosystem.

This is preferred to be a hybrid role located at our Res Med headquarters in San Diego, CA.

Employer will not sponsor applicants for employment visa status.

As a Data Platform Engineer Intern, you will learn by doing. With support from your team, you will:

• Assist in building and maintaining data pipelines for ingesting, transforming, and delivering data using Python, SQL, and modern data tools.
• Contribute to analytics and data models in Snowflake using dbt or similar transformation frameworks.
• Learn how cloud‑native data platforms are built and operated on AWS.
• Support testing, documentation, and monitoring efforts to help ensure data quality and reliability.
• Collaborate with data engineers, analytics engineers, and data scientists to understand how data is used across the organization.
• Participate in code reviews, team discussions, and design sessions to build strong engineering fundamentals.
• Gain exposure to data platforms that support analytics, reporting, and early‑stage ML/AI use cases.

• Ability to work full‑time for 12 weeks during Summer 2025; program dates are May 18th – August 7th, 2026, OR June 15th – September 4th, 2026
• Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related STEM field.
• Foundational knowledge of programming concepts; experience with Python, SQL, or similar languages through coursework, projects, or personal learning.
• Interest in data engineering, data platforms, and cloud technologies.
• Basic understanding of software engineering concepts such as version control, testing, or debugging (academic or personal experience is fine).
• Curiosity, willingness to learn, and comfort asking questions in a collaborative environment.

• Exposure to SQL‑based data analysis or modeling through coursework or projects.
• Familiarity with cloud platforms (AWS preferred) from classes, labs, or personal projects.
• Experience with Git/Git Hub through school or open‑source work.
• Coursework or projects related to databases, distributed systems, or data engineering.
• Interest in analytics, machine learning, or AI‑driven data use…