Data Analytics · SQL ETL · Python · Power BI
SAYAN
MANNA
Computer Science (Data Science) Graduate
ABOUT ME
Computer Science (Data Science) graduate with hands-on experience in SQL-based data preparation, Power BI dashboard development, and Python-driven exploratory data analysis through real-world and technical projects.
Proven ability to transform structured and semi-structured data into KPI-driven insights, trend analysis, and executive reports. Strong foundation in ETL workflows, dashboard automation, and stakeholder-oriented analytics within product and operational contexts.
TECH STACK
PROJECT EXPERIENCE
Vendor Performance Analysis — Retail Inventory & Sales
Analyzed vendor efficiency and profitability to support strategic purchasing and inventory decisions for retail operations.
- Developed and optimized a complex SQL ETL pipeline to build an aggregated summary table from multiple tables. Improved query performance using CTEs and data filtering, significantly reducing processing time for large datasets.
- Conducted Exploratory Data Analysis and Hypothesis Testing in Python to evaluate vendor profitability, pricing strategy effectiveness, and inventory turnover.
- Identified over-dependence on top 10 vendors (65.7% of purchases) and uncovered $2.71M in unsold inventory from low-performing vendors, recommending diversification and inventory optimization.
- Built interactive Power BI dashboards to visualize vendor performance, profit margins, bulk purchasing impact (72% cost reduction), and actionable insights for decision-makers.
Banking Data Analytics
End-to-end analytics pipeline processing 10,000+ banking transaction records and 2,000+ customer profiles to surface revenue trends and customer behavior insights.
- Performed ETL including data extraction, cleaning (6% missing values), and transformation using Python
- Developed 15+ SQL queries using joins and aggregations to extract KPIs including customer contribution and transaction frequency
- Applied Pareto analysis identifying top 20% customers contributing 60% revenue
- Built interactive Power BI dashboards tracking monthly trends and customer segmentation
OLA Performance Analytics
Operational intelligence dashboard analyzing 10K+ ride-level transactional records to evaluate booking success, cancellations, customer ratings, and revenue trends.
- Built optimized SQL queries using joins, aggregations, and filters to prepare clean, analysis-ready datasets
- Designed Power BI dashboards tracking KPIs: cancellation rate, average rating, and revenue per ride
- Identified key cancellation drivers generating insights with potential to improve operational efficiency by 10–15%
- Delivered documented insights and dashboards to support data-driven business and product decisions
CERTIFICATIONS
LET'S
CONNECT
Open to Data Analyst roles, full-time opportunities, and collaborative analytics projects. Let's build something insight-driven together.