Data Engineer Intern (Summer 2025)

As a Data Engineer Intern at Electra, you will engage in developing automation for data processing, analyze and maintain data pipelines, and write libraries for data visualization using Python. Your role will also include crafting REST API endpoints with FastAPI and creating new visualizations for easy interaction with our scientific database, providing you with valuable hands-on experience in the field.

To be eligible for the Data Engineer Intern position at Electra, you must be actively enrolled in an accredited college or university pursuing an undergraduate or graduate degree in Mechanical Engineering, Chemical Engineering, Electrical Engineering, or a related technical field. Familiarity with relational databases, SQL, PostgreSQL, and programming in Python and Javascript is also essential.

Electra seeks Data Engineer Interns with a solid understanding of relational databases, proficiency in SQL and PostgreSQL, and expertise in Python. You'll also need experience with version control software like Git and familiarity with both vanilla Javascript and Typescript. The ideal candidate will have a passion for software architecture and API programming.

Interns at Electra can expect competitive compensation based on their education level. Undergraduate students will earn $29.00 per hour, while graduate students will receive $32.00 per hour. While you will be responsible for your housing solutions during the summer, Electra will assist with suggestions and connections with fellow interns.

The Data Engineer Intern position at Electra offers you hands-on experience in a pioneering industry, the opportunity to work with novel technologies, and collaborate with diverse teams. This experience will not only enhance your technical skills but also expand your professional network, ultimately boosting your career prospects in the cutting-edge field of data engineering.

To handle data processing automation, discuss your experience with tools and languages, like Python, that facilitate coding automated scripts for repetitive tasks. Explain how you’ve designed data pipelines, ensured data integrity, and the importance of documentation in automation processes.

When answering this question, highlight specific projects or coursework involving SQL and relational databases. Discuss how you’ve utilized SQL for data queries, management, and relational design to support data-driven decisions in your prior experiences.

In your response, illustrate your understanding of API architecture. Provide examples of how you’ve previously employed REST APIs in projects, emphasizing any experience with FastAPI or similar frameworks. Highlight how APIs enhance data interchange and integration with external systems.

Talk about your experience using Git for version control, touching on best practices like committing frequently with descriptive messages, using branches for features and bug fixes, and collaborating with others through pull requests. Emphasize how these strategies improve team collaboration and project organization.

Detail a specific project in which you employed Python to analyze datasets. Discuss the tools and libraries used, the challenges faced (like data cleaning or performance issues), and the insights gained from the analysis. Demonstrating problem-solving skills and the ability to derive actionable conclusions is key.

Discuss your approach to writing clean, maintainable code and the importance of writing unit tests for data processing scripts. Highlight your experience with debugging and code reviews, showcasing your understanding of quality control in software development.

Express your enthusiasm for Electra’s mission of reducing carbon emissions and how data engineering plays a pivotal role in that. Share your passion for sustainability and how the chance to work on innovative solutions aligns with your career aspirations in technology and environmental impact.

Outline your task prioritization strategy, possibly touching on methods like the Eisenhower box or Kanban boards. Discuss how you assess deadlines, project complexity, and team dependencies to manage your schedule effectively while maintaining quality work.

Mention specific tools and libraries you’ve worked with, such as Matplotlib, Seaborn, or Tableau, to elaborate on how they aided in turning complex data information into visually digestible formats. Explain how these visualizations facilitated better decision-making or insights for stakeholders.

Share your strategies for staying current in the field, like following industry blogs, participating in forums, enrolling in online courses, or attending meetups and conferences. Discuss how this proactive engagement shapes your skill set and ensures you remain competitive in the rapidly evolving tech landscape.