Data Engineering Intern (Medtech)

TL;DR

Data Engineering Intern (Microsoft Fabric): Designing, building, and maintaining scalable data pipelines and ETL processes with an accent on the medallion architecture and cloud-native Azure environments. Focus on implementing data models using dbt and PySpark to ensure data reliability and accessibility for analytics.

Location: Remote (Must be based in the USA)

Salary: Up to $25/hr

Company

hirify.global is an online medical clinic at the forefront of transforming healthcare and enhancing longevity through scientific advancements.

What you will do

• Design and maintain scalable data pipelines and ETL processes using Microsoft Fabric (Notebooks, Pipelines, Dataflows).
• Integrate data from internal and external sources ensuring quality across the medallion architecture.
• Develop and maintain data models and transformations using dbt within a Fabric lakehouse.
• Collaborate with engineers and analysts to translate business requirements into technical data solutions.
• Monitor and optimize data infrastructure for performance and scalability in a cloud-native Azure environment.
• Follow engineering best practices for version control and CI/CD using GitHub.

Requirements

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or a related field.
• Strong foundational knowledge of SQL and experience querying relational databases.
• Proficiency in Python; PySpark experience is a plus.
• Understanding of data modeling, warehousing, or analytics engineering (dbt or medallion architecture).
• Experience with GitHub for version control and modern engineering workflows.
• Must have a stable internet connection and access to a PC/laptop.

Culture & Benefits

• Remote-first work environment.
• Budget for necessary technology tools (laptop, monitor, and special software).
• Company-sponsored activities to foster engagement, both virtual and in-person.
• Opportunity to work with a modern data stack including Microsoft Fabric, dbt, and PySpark.