Data Engineering Intern: Build Pipelines & Insights

stvinc; Chicago (IL); On-site; USD 48,000 - 54,000; Part time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

401(k)

Paid Time Off

Parental Leave

Education reimbursement

Professional licensing support

Paid holidays

Job summary

STV is seeking an enthusiastic Data Engineering Intern to join our team in Chicago. This internship offers undergraduate and graduate-level candidates hands-on experience designing, developing, and maintaining data pipelines to support data-driven initiatives.

You will work with our data engineering team to gain experience using Azure Data Factory and PySpark, and to build data models, ETL processes, and SQL-driven dashboards for stakeholders.

Qualifications

• Currently pursuing a bachelor's or master's degree in Computer Science, Data Engineering, or Software Engineering.
• Hands-on experience with ETL processes and tools.
• Familiarity with Python, SQL, and data libraries.
• Basic Git version control knowledge.
• Understanding of data modeling and databases (MySQL, SQL Server, MongoDB).

Responsibilities

• Develop and optimize data transformation pipelines from diverse sources.
• Collaborate with team to advance data utilization across projects.
• Create data visualizations to share findings with stakeholders.
• Assist in building applications to display AEC data.
• Conduct exploratory data analysis to uncover patterns.
• Document methodologies to enable knowledge transfer.

Skills

ETL processes

Python

SQL

Git

Pandas/Numpy

Data modeling

Education

Bachelor's or Master's in CS/Data Eng/SE

Tools

Azure Data Factory

PySpark

MongoDB

MySQL

SQL Server

Job description

STV is seeking an enthusiastic Data Engineering Intern to join our team in Chicago. This internship offers undergraduate and graduate-level candidates hands-on experience designing, developing, and maintaining data pipelines to support data-driven initiatives.

You will work with our data engineering team to gain experience using Azure Data Factory and PySpark, and to build data models, ETL processes, and SQL-driven dashboards for stakeholders.