Data Analyst

Data Analyst

Roles and Responsibilities:

• Data Collection & Extraction:

  Identify, collect, and extract data from various sources (databases, APIs, spreadsheets, etc.) ensuring data accuracy and completeness.

• Data Cleaning & Transformation:

  Clean, transform, and prepare large datasets for analysis, identifying and resolving inconsistencies or errors.

• Data Analysis & Modeling:

  Perform exploratory data analysis, statistical analysis, and create data models to uncover trends, patterns, and insights relevant to business questions.

• Report & Dashboard Development:

  Design, develop, and maintain interactive reports, dashboards, and data visualizations using BI tools (e.g., Tableau, Power BI, Qlik Sense) to present findings clearly and effectively.

• SQL Querying:

  Write and optimize complex SQL queries to retrieve and manipulate data from relational databases.

• Performance Monitoring:

  Monitor key performance indicators (KPIs) and identify areas for business improvement or operational efficiencies based on data analysis.

• Insights & Recommendations:

  Translate complex data findings into clear, concise, and actionable recommendations for business stakeholders.

• Troubleshooting:

  Identify and troubleshoot data discrepancies, reporting issues, and data pipeline challenges.

• Collaboration:

  Work closely with business users, product managers, developers, and other teams to understand data requirements and deliver relevant analytical solutions.

• Documentation:

  Create and maintain documentation for data sources, dashboards, reports, and analytical methodologies.

Preferred Candidate Profile:

• Data Analysis Skills:

  Strong analytical and problem-solving skills with the ability to interpret large datasets and provide actionable insights.

• SQL Proficiency:

  Advanced proficiency in

  SQL

  for data extraction, manipulation, and analysis.

• BI Tools:

  Hands-on experience with Business Intelligence and data visualization tools (e.g.,

  Tableau, Microsoft Power BI, Qlik Sense

  ).

• Data Storytelling:

  Ability to present complex data findings in a clear, concise, and compelling manner to both technical and non-technical audiences.

• Statistical Knowledge:

  Understanding of statistical concepts and their application in data analysis.

• Programming (Plus):

  Familiarity with scripting languages like

  Python

  or

  R

  for data analysis and manipulation is an advantage.

• Data Warehousing (Plus):

  Basic understanding of data warehousing concepts and ETL processes.

• Communication:

  Excellent verbal and written communication skills to collaborate effectively with cross-functional teams.

• Education:

  Bachelor's degree in Data Science, Statistics, Computer Science, Economics, Business Analytics, or a related quantitative field.