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Data Analyst Intern

IG Tech

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Original Job Summary

About Data Analyst Intern

INLIGHN TECH provides hands-on, project-driven virtual internships bridging the gap between academics and industry. The Data Analyst Intern role is designed to develop core skills in data analysis, reporting, and visualization to support data-driven decision making.

Internship Overview

As a Data Analyst Intern, you will gather, clean, transform, and analyze data. You will collaborate with business and product teams to create dashboards, reports, interpret trends, and deliver actionable insights.

Qualifications

  • Pursuing or recently completed a degree in Computer Science, Statistics, Economics, Business Analytics, Mathematics or related fields
  • Proficiency in Excel and SQL for data manipulation and reporting
  • Familiarity with data visualization tools like Power BI, Tableau or Google Data Studio
  • Basic understanding of statistics and data interpretation
  • Exposure to Python (Pandas, NumPy, Matplotlib) is a plus
  • Strong analytical mindset, attention to detail, and problem-solving skills

What You’ll Gain

  • Practical exposure to real-world datasets
  • Experience building dashboards and reports with industry-standard tools
  • Internship Certificate and Letter of Recommendation
  • A portfolio of analysis projects to showcase skills
  • A foundation for a career in Data Analytics and Business Intelligence

Key skills/competency

  • Data Analysis
  • SQL
  • Excel
  • Data Visualization
  • Power BI
  • Tableau
  • Python
  • Statistics
  • Reporting
  • Problem-solving

How to Get Hired at IG Tech

🎯 Tips for Getting Hired

  • Tailor your resume: Highlight data analysis projects and technical tools.
  • Showcase relevant skills: Demonstrate Excel, SQL, and visualization expertise.
  • Prepare for technical interviews: Brush up on statistics and Python basics.
  • Research INLIGHN TECH: Understand the company culture and internship structure.

📝 Interview Preparation Advice

Technical Preparation

Practice SQL queries and Excel functions.
Review data cleaning and transformation techniques.
Familiarize with Power BI and Tableau basics.
Refresh Python libraries for data analysis.

Behavioral Questions

Describe a challenging data project.
Explain problem-solving approach in analysis.
Discuss teamwork examples in previous projects.
Share experiences with learning new tools.