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Senior Data Analyst
Skills
SQL QueriesTableauPythonNumPypandasData AnalysisProcess ImprovementPower BI
12 days ago
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Important information
Contract type:
Permanent
Location:
Noida, Uttar Pradesh, India
Work mode:
Onsite
Published on:
15 October 2026
What they need
- 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.
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