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Divyanshu Pandey
Mid-level Data Analyst specializing in analytics, reporting, and operational insights
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Analytics candidate with hands-on experience turning messy retail/customer data into clean reporting tables using SQL and PostgreSQL, then extending the work into Python-based reusable analysis workflows. They have applied segmentation, cohort analysis, and retention metric design to support dashboards and improve targeting, engagement, and repeat purchase performance.
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Key Strengths
- Built SQL reporting tables from messy multi-table customer shopping data
- Strong data quality validation using totals, row counts, and raw-data spot checks
- Practical SQL performance optimization through selective columns and efficient joins
- Structured Python analytics workflows with reusable functions
- Customer segmentation and cohort analysis for revenue and retention insights
- Defined and operationalized retention metrics for dashboard adoption
- Translated analysis into marketing targeting and customer retention decisions
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