Data Science Engineer Intern (Summer 2026)

Role Description

As a Dropbox Data Engineer Intern in the Customer Strategy Organization (CSO), you’ll be part of a top-notch learning experience working alongside experienced individuals from diverse backgrounds. You’ll have a dedicated Dropboxer mentoring you every step of the way as you build foundational and practical data engineering skills.

Our goal is to create a robust and impactful learning experience. You can expect to learn how modern data platforms and pipelines are designed, built, and operated to support analytics, data science, and business decision-making across our revenue, marketing, and product teams. We're all about feedback, so anticipate continual guidance refining your approach. And while you're forging your path, we're here to ensure you bond with like-minded interns, discover mentors, and lay the bricks for an expansive professional network.

We will work with you to accommodate your school end and start dates, culminating in a minimum of a 12-week long internship.

In this role, you will work with a wide range of data sources—including customer surveys, support tickets, call transcripts, and product usage data—to help design, build, and maintain reliable data pipelines and datasets. You will gain hands-on experience with data ingestion, transformation, and quality validation.

You are a self-starter with a strong affinity for problem-solving, a solid foundation in programming and data concepts, and an eagerness to learn scalable, production-ready data systems. You bring a curious, detail-oriented mindset and are excited to experiment with new tools, technologies, and engineering patterns.

Responsibilities

• Design, build, and maintain data pipelines that ingest and process structured and unstructured data sources such as surveys, support tickets, call transcripts, and product usage data.
• Develop and experiment with scalable data processing workflows that support downstream analytics, machine learning, and large language model (LLM) use cases.
• Transform, validate, and model large, multi-dimensional customer behavior and usage datasets to ensure they are reliable, well-structured, and analytics-ready.
• Partner with data scientists, analysts, and business stakeholders to enable clear understanding and effective use of data through well-defined datasets, documentation, and data quality standards.
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