Data Analyst
Job Description
Data Analyst – Job Description:
A Data Analyst collects, cleans, analyzes, and interprets data to help organizations make informed business decisions. The role involves identifying trends, creating reports and dashboards, and communicating insights to stakeholders.
Key Responsibilities:
Collect, clean, validate, and transform data from multiple sources.
Analyze large datasets to identify trends, patterns, and business opportunities.
Create interactive dashboards and reports using Power BI or Tableau.
Write SQL queries to extract, join, and manipulate data from databases.
Use Excel for data analysis, pivot tables, formulas, and reporting.
Perform exploratory data analysis (EDA) and investigate data discrepancies.
Track KPIs and prepare regular business performance reports.
Collaborate with business teams to understand requirements and deliver insights.
Automate recurring reports and improve data quality and reporting processes.
Qualifications:
Bachelor's degree in Computer Science, Statistics, Mathematics, Business Analytics, or a related field.
Experience working with datasets, reporting tools, and databases.
Strong analytical thinking and problem-solving skills.
Ability to communicate complex data insights clearly.
Relevant internship or professional experience may be preferred, depending on the position.
Experience Requirements:
Entry-level: 0–2 years of experience; Excel, SQL fundamentals, and basic visualization.
Mid-level: 2–5 years; advanced SQL, dashboard development, KPI analysis, and business reporting.
Senior-level: 5+ years; advanced analytics, stakeholder management, reporting strategy, and mentoring.
Required Technical Skills:
Skill Expected knowledge
SQL Joins, subqueries, CTEs, window functions, aggregations
Excel Pivot tables, XLOOKUP, formulas, charts
Power BI / Tableau Dashboards, data visualization, KPI reporting
Python Pandas, NumPy, data cleaning and analysis
Statistics Mean, median, correlation, distributions, hypothesis testing
Databases SQL Server, MySQL, PostgreSQL, or similar
Data cleaning Missing values, duplicates, validation, transformation
Communication Reporting insights and explaining findings
Present analytical findings and recommendations to stakeholders.