With a strong emphasis on technical expertise and analytical skills, this role is ideal for candidates with a bachelor's degree in Computer Science, Mathematics, or Information Technology. Key competencies include advanced proficiency in data manipulation and analysis using Excel, SQL, and programming languages such as Java, PHP, C++, or JavaScript (Node.js). Experience with data visualization tools like Tableau, Power BI, or Qlik is essential, along with a solid understanding of statistical techniques and ETL processes to ensure data quality and integrity. This role also requires the ability to design and manage data pipelines while producing insights that drive business decisions.

Soft skills are equally vital, with a focus on analytical thinking, effective communication, and cross-functional collaboration with teams like marketing, sales, and IT. The ideal candidate should excel at interpreting complex datasets, asking the right questions, and identifying trends that provide actionable insights. Depending on experience, the position suits either entry-level professionals with 0-2 years of experience or mid-level experts with 2-5 years, capable of managing complex datasets and projects independently.

• Bachelor’s Degree: Most employers require a bachelor’s degree in a related field such as:
  • Computer Science
  • Mathematics
  • Information Technology

• Data Manipulation & Analysis:
  • Excel: Advanced functions like pivot tables, vLOOKUP, data cleaning, and complex formulas.
  • SQL: Proficient in querying databases, creating reports, and managing large datasets.
  • Programming Languages: Familiarity with Java, PHP, C++, JavaScript (Node.js)
• Data Visualization:
  • BI Tools: Proficient with tools such as Tableau, Power BI, or Qlik for creating visual reports and dashboards.
• Statistical Analysis: Ability to apply statistical techniques like regression, hypothesis testing, and time series analysis.
• Data Warehousing & ETL: Understanding of data pipelines, ETL processes, and how to extract, transform, and load data efficiently.
• Data Quality Assurance: Ensuring accuracy, consistency, and reliability of datasets.

• Analytical Thinking: Strong problem-solving abilities, with attention to detail in data interpretation.
• Communication Skills: Ability to translate complex data findings into understandable insights for stakeholders.
• Collaboration: Ability to work cross-functionally with teams such as sales, business development, marketing, and IT.
• Critical Thinking: Ability to ask the right questions and identify trends that can lead to business insights.

• Entry-Level: 0-2 years of experience, typically for fresh graduates or those transitioning into data roles.
• Mid-Level: 2-5 years of experience, demonstrating an ability to handle complex datasets and projects independently.