Data Engineering Intern (Fall 2026)

Hone is an online medical clinic focused on transforming healthcare and enhancing longevity through scientific advancements. The Data Engineering Intern will support the design, development, and maintenance of data systems and pipelines, working closely with engineers, analysts, and product teams to ensure data accuracy, reliability, and accessibility. This role offers hands-on experience with modern data tools and scalable data pipeline development.

Responsibilities:

• Design, build, and maintain scalable data pipelines and ETL processes using Microsoft Fabric (Notebooks, Pipelines, Dataflows) to support analytics, reporting, and product use cases
• Integrate data from multiple internal and external sources, ensuring quality, consistency, and reliability across the medallion architecture
• Develop and maintain data models and transformations using dbt, contributing to bronze, silver, and gold layer modeling in the Fabric lakehouse
• Collaborate with engineers, analysts, and product teams to translate business requirements into technical data solutions — communicating through Slack and tracking work in Azure DevOps (ADO)
• Participate in data quality checks, testing, validation, and performance optimization across pipeline and model layers
• Monitor, optimize, and troubleshoot data infrastructure for performance and scalability in a cloud-native Azure environment
• Follow engineering best practices around version control and CI/CD using GitHub, including branch management, pull requests, and code review
• Contribute to data documentation and ensure best practices around data governance, reliability, and scalability
• Contribute to the continuous improvement of data engineering processes and tools

Requirements:

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, Information Systems, or a related field
• Strong foundational knowledge of SQL and experience querying relational databases
• Proficiency in Python and a strong interest in distributed data processing
• Understanding of data modeling, data warehousing, or analytics engineering concepts
• Exposure to or coursework involving data pipeline orchestration or ETL development
• Comfort working in a modern engineering workflow — GitHub for version control, ADO for ticketing, and Slack for async team communication
• Strong analytical thinking, problem-solving abilities, and attention to detail
• Eagerness to learn new technologies and frameworks, with a focus on self-improvement
• Effective communication skills and the ability to work collaboratively in a remote cross-functional environment
• A stable internet connection and access to a PC/laptop
• PySpark experience is a plus
• Familiarity with dbt or medallion architecture is a plus
• Experience with Microsoft Fabric, Azure Data Factory, or similar cloud pipeline tooling is a bonus