Senior Data Analyst
Summary
Senior Data Analyst based in Noida, India who collects and analyzes large datasets, builds interactive Power BI/Tableau dashboards, writes complex SQL, and automates reporting with Python to deliver KPI insights to stakeholders. Also mentors junior analysts; requires 4-7 years in data analytics/BI.
Collect, clean, validate, and analyze large datasets from multiple sources.
Develop and maintain interactive dashboards and reports using Power BI or Tableau.
Write complex SQL queries to extract, transform, and analyze data.
Perform exploratory data analysis (EDA) to identify trends, patterns, and business opportunities.
Analyze business KPIs and provide actionable insights to stakeholders.
Automate reporting processes using Python, SQL, and other analytical tools.
Design and implement data models to support business intelligence and reporting requirements.
Collaborate with business, product, marketing, finance, and operations teams to understand analytical requirements.
Conduct statistical analysis, forecasting, and trend analysis.
Ensure data accuracy, consistency, integrity, and quality across reports.
Create and maintain documentation for dashboards, reports, and analytical processes.
Present analytical findings and recommendations to senior management.
Identify opportunities for process improvement and operational efficiency.
Mentor junior data analysts and review their analytical work.
Support business strategy through data-driven insights and performance analysis.
Requirements
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Economics, Data Analytics, or a related field.
4–7 years of professional experience in data analytics or business intelligence.
Advanced proficiency in SQL, including joins, subqueries, CTEs, window functions, and query optimization.
Strong knowledge of Python, including Pandas, NumPy, and data manipulation.
Hands-on experience with Power BI, Tableau, or similar BI tools.
Advanced Excel skills, including Pivot Tables, XLOOKUP, Power Query, and complex formulas.
Strong understanding of statistical analysis, hypothesis testing, and data interpretation.
Experience in data cleaning, transformation, validation, and reporting.
Knowledge of ETL processes, data warehousing, and data modeling.
Ability to translate business requirements into analytical solutions.
Excellent communication, presentation, and stakeholder management skills.
Strong analytical thinking, problem-solving, and attention to detail.