Data Engineering Intern: Build Scalable Pipelines

Greystar Worldwide, LLC; Michigan; On-site; USD 43,000 - 62,000; Part time

Benefits offered by this job

Employee Assistance Program

Paid sick time

Job summary

Greystar is offering a Data Engineering Internship in the United States for a graduate student. You will build scalable data pipelines using Python, PySpark, and SQL, collaborate with data scientists, and help operationalize data quality across systems.

This 10-week program provides hands-on learning with real projects and mentorship. You will engage with teams across Investment, IT, and Analytics, present a Final Capstone, and gain exposure to enterprise data architecture while developing

Qualifications

• Quantitative background and analytical problem solving ability.
• Strong Python, PySpark, SQL, and GitHub skills.
• Excellent written and verbal communication with attention to detail.
• Experience with Azure DevOps, Slack, Teams, Cursor, Claude or Copilot is a plus.
• Enrolled in a graduate degree program in Data Engineering or Data Analytics.
• Graduating between December 2027 and May 2028.

Responsibilities

• Design and develop data pipelines using Python, PySpark, SQL and other tools.
• Use coding agents to speed up delivery of data pipelines.
• Create enterprise-level code and repeatable processes.
• Operationalize data pipelines and enhance data quality.
• Collaborate with data scientists and data architects on projects.
• Partner with internal teams to support initiatives and improve performance.
• Generate documentation ready for business use.

Skills

Python

PySpark

SQL

GitHub

Excel

PowerPoint

Communication

Attention to detail

Education

Graduate degree in Data Engineering/Data Analytics

Graduating December 2027 - May 2028

Tools

Azure DevOps

Slack

Microsoft Teams

Cursor

Claude

GitHub Copilot

Job description

Greystar is offering a Data Engineering Internship in the United States for a graduate student. You will build scalable data pipelines using Python, PySpark, and SQL, collaborate with data scientists, and help operationalize data quality across systems.

This 10-week program provides hands-on learning with real projects and mentorship. You will engage with teams across Investment, IT, and Analytics, present a Final Capstone, and gain exposure to enterprise data architecture while developing