About
Data Analyst and Computer Science graduate (B.Tech, 2025) with hands-on expertise in SQL, Python, Power BI, and Excel — skilled in gathering and analyzing data, building dashboards, and supporting business performance tracking. Developed an end-to-end analytics project analyzing 100,000+ UPI transactions — implementing anomaly detection, defining metric logic for 20+ KPIs, and building 4 Power BI dashboards for executive and operational reporting. Strong analytical mindset with a passion for data-driven decision-making, ownership mentality, and excellent attention to detail. Available to join immediately.
Skills & Expertise (41)
Work Experience
Dashboard
UPI Transaction Analytics & Fraud Risk Analysis
Mar 2026 - Jun 2026
Designed a 7-table normalized SQL Server schema with 8 FK constraints, composite indexes, and data validation rules — managing 139,500+ records across 7 entity types to support analytics and business performance tracking. Authored 20+ T-SQL queries (JOINs, CTEs, Window Functions, Subqueries) to collect, analyze, and report KPIs — transaction trends, fraud rates, merchant revenue, customer segmentation — for stakeholder reporting. Conducted end-to-end EDA in Python (Pandas, NumPy) — performing data quality audits, anomaly detection (NULL checks, outlier identification), and generating 12 visualizations (Matplotlib, Seaborn) to surface data errors and support decision-making. Applied 6 hypothesis tests (T-test, ANOVA, Chi-square, Pearson Correlation) using SciPy — confirming 15× higher fraud risk on rooted devices (20.69% vs 1.39%, p=0.0000) and generating anomaly insights for incident prioritization. Built 4 Power BI dashboards (Executive, Merchant, Fraud Monitoring, Fraud Analysis) using DAX, Power Query, and Star Schema modeling — enabling business performance tracking with KPI cards, drill-through filters, and tooltips for executive stakeholders. Performed root cause analysis revealing 3.1% of rooted devices drive 32.8% of fraud — developed incident prioritization logic and 3 actionable recommendations with ₹1,49,500 estimated business impact.
GitHub
Customer Analysis for Retail
Jan 2026 - Mar 2026
Gathered, merged, and validated customer, transaction, and product datasets (50,000+ records) using Python Pandas — resolving nulls, correcting data types, and standardizing fields to support business requirement documentation. Analyzed and reported customer purchasing behavior using Matplotlib and Seaborn — identifying 3 customer segments and seasonal demand trends through market and competitive trend analysis. Conducted business performance analysis on seasonal buying patterns — generating insights and supporting data-driven decision-making across product and customer segments.
Education
Bachelor of Technology (B.Tech) – Computer Science Engineering - Panipat Institute of Engineering & Technology, Kurukshetra University
2022 - 2025 · Afghanistan
Certifications
SQL (Intermediate)
HackerRank · 2026
Python (Basic)
HackerRank · 2026
Data Analytics Job Simulation
Deloitte Forage · 2026
Power BI for Beginners
Simplilearn · 2026
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Citizen
Relocation
Open to Relocation
Skills (41)
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