Posted 1mo ago

Data Engineering Internship

@ Mast-Jägermeister US
White Plains, New York, United States
$25/hrHybridMultiple Commitments Available
Responsibilities:Assisting data, Enhancing models, Implementing Copilot
Requirements Summary:Pursuing a degree in data-related field; strong SQL, Power BI, DAX, and Python; interest in data analytics and AI; collaborative, problem-solving mindset.
Technical Tools Mentioned:SQL, Power BI, DAX, Python, Microsoft Fabric, Copilot AI
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Job Description

The Data Engineering Intern will support the Business Technology team in enhancing our data foundation through hands‑on work with data warehousing, reporting, machine learning optimization, and Copilot AI enablement. This role is ideal for a student passionate about analytics, data engineering, and next‑generation BI tools who wants meaningful project ownership and exposure to enterprise‑level data environments.

This is a hybrid internship – ability to work in our HQ White Plains office and remotely.

Principal Duties and Responsibilities:

  • Assist with data warehouse cleanup, optimization, and documentation to improve data reliability and performance.
  • Support Power BI semantic model enhancements, including DAX optimization and report performance tuning.
  • Partner with the team to implement Copilot AI enablement for Power BI, accelerating insights for business users.
  • Contribute to machine learning model optimization, including feature engineering, testing, and performance assessments.
  • Collaborate with cross‑functional teams to understand data needs and ensure smooth data flows within the Business Technology landscape.
  • Participate in sprint planning, team stand‑ups, and review cycles as part of the BT project workflow.

Key Learnings:

  • Data reporting methodologies and best practices
  • Data modeling and semantic layer design
  • Microsoft Fabric and modern data architecture approaches
  • Power BI Copilot and AI‑assisted analytics
  • Machine learning fundamentals, including model refinement and evaluation
  • Exposure to enterprise data management, governance, and automation strategies