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Title: From Raw Transactions to Management KPIs: A Recurring Financial Reporting System
Description:
Built a repeatable reporting system that turns raw operational data — sales, loan portfolio, transactions — into a standard set of financial KPIs management reviews every month, replacing ad hoc spreadsheet rebuilds with a consistent, repeatable process.
Challenge: Management wanted a reliable monthly view of business performance, but the underlying data lived across multiple raw exports with no single definition of each metric — every report was rebuilt by hand, and numbers didn't always match between departments.
Approach: Defined a single set of KPI formulas once (revenue, portfolio yield, collection rate, cost-income ratio, EBITDA margin), then built a structured process that pulls raw data, applies the same transformations every cycle, and outputs the same report format month after month.
What I did: Designed the data structure and metric definitions, standardized how raw fields map to each KPI, and built the process to be refreshed rather than rebuilt — a new month's numbers flow in without re-deriving the logic from scratch.
Outcome: Turned a multi-day manual reporting cycle into a same-day refresh, with every department working from the same numbers and the same definitions.
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I analyzed 3M+ job postings across 161 countries and 93K companies to identify hiring trends, in-demand roles, work-mode patterns, and salary insights.
I used Python for data cleaning and preprocessing, SQL for analysis, and Power BI to build an interactive dashboard.
Key areas analyzed:
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• Remote vs. onsite/hybrid work patterns
• Average salary trends
• Monthly hiring trends
This project helped me turn a large dataset into clear, actionable insights for understanding the global job market.
PROFESSIONAL SUMMARY
Data-driven professional with strong expertise in Data Analysis, Excel, and SQL. Proficient in building data dashboards, performing complex data cleaning, and deriving actionable business insights. Demonstrated ability to work with large datasets (1400+ records) and create comprehensive analytics solutions. Seeking part-time opportunities to leverage analytical skills while expanding expertise in data-driven decision-making.
TECHNICAL SKILLS
Data Analysis & Visualization: Excel (Advanced), SQL (Intermediate), Data Cleaning, Data Modeling
Excel Expertise: Advanced formulas, VLOOKUP, Pivot Tables, Dashboards, Data Validation, Conditional Formatting
SQL Skills: Query writing, Data extraction, Database analysis
Programming: Python (Basic).
Project Links :
an Excel-based data analysis project focused on Sales Performance and Employee Analysis.
🔹 Sales Analysis
• 50 sales records
• Product & category performance
• Regional sales analysis
• Total sales and order KPIs
• Date-based analysis
• Pivot Tables & charts
🔹 Employee Analysis
• 30 employee records
• Department & job-title analysis
• Salary analysis
• Years of experience
• Salary Rating & Experience Status
• VLOOKUP-based analysis
🛠️ Excel Skills Used
• Data Cleaning
• Formulas & Functions
• VLOOKUP
• Pivot Tables
• KPI Analysis
• Data Categorization
• Business Reporting
This project helped me practice turning raw business data into structured analysis and actionable insights.
Project Link : https://github.com/rajveer7489/sales-employee-analysis
