Data Analyst

At Link Group, we specialize in building tech teams for Fortune 500 companies and some of the world's most exciting startups. Our mission is to connect talented professionals with opportunities that align with their skills, interests, and career aspirations.

We are currently looking for a Data Analyst to join our team and help transform raw data into actionable insights for global clients.

About the Project

The project focuses on analyzing complex datasets from the finance/stock exchange industry to provide insights that drive strategic decision-making. You will work closely with business stakeholders to deliver impactful data visualizations and reports.

Tech Stack

  • Data Analysis Tools: Python, R, SQL
  • Data Visualization: Tableau, Power BI, Matplotlib, Seaborn
  • Databases: SQL/NoSQL (MySQL, PostgreSQL, MongoDB)
  • ETL Tools: Alteryx, Talend
  • Cloud Platforms: AWS, GCP, Azure

What We Offer

  • Tailored opportunities to match your professional interests and goals.
  • A dynamic and collaborative work environment.
  • Access to exciting and diverse projects for global clients.
  • Competitive compensation aligned with your expectations.
  • Ongoing opportunities for professional growth and development.

If you're ready to take on new challenges and work on groundbreaking projects in the IT industry, we'd love to hear from you!

Apply today and join us at Link Group to make an impact!

Must-Have Qualifications

  • At least 3+ years of experience in data analysis or a related field.
  • Strong proficiency in SQL for querying and managing data.
  • Experience with data visualization tools like Tableau or Power BI.
  • Proficiency in Python or R for data analysis.
  • Excellent analytical and problem-solving skills.
  • Strong understanding of statistical methods and data modeling.
  • Fluent English communication skills.

Nice to Have

  • Familiarity with ETL processes and tools like Alteryx or Talend.
  • Experience with cloud platforms such as AWS, GCP, or Azure.
  • Knowledge of machine learning concepts and tools.
  • Background in finance or experience working with financial datasets.
  • Academic degree in Statistics, Data Science, or a related field.

See also

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