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Data Analyst with 4+ years in SQL & Power BI
Results-driven Data Analyst with finance domain experience at Deloitte, adept in data transformation, Power Query, and dashboard development. Proficient in SQL, Python, Power BI, and Excel for exploratory data analysis, KPI dashboard creation, and delivering data-driven insights that support business performance and decision-making. Skilled in managing and reconciling large financial datasets, automating reporting workflows, and creating impactful dashboards to drive informed business decisions.
Amity University
BCA · Data Analytics & Reporting
January 1, 2023 – January 1, 2026
Gujarat University
M.COM, B.COM
N/A – Present
Promptitude (Client: Deloitte/Swiggy)
Finance Analyst
August 1, 2021 – June 1, 2023
Bengaluru, Karnataka, India
Credit Card Transaction Analysis
June 17, 2026 – Present
Developed optimized SQL queries (joins, subqueries, CTEs) to extract and transform large-scale transaction datasets, improving data retrieval efficiency and analysis speed. Performed data cleaning and analysis using Python (Pandas, NumPy), identifying customer spending patterns and behavior trends across multiple segments. Analyzed transaction data to uncover key revenue drivers, enabling data-driven business decision-making. Delivered actionable insights that contributed to improving revenue strategy and customer targeting effectiveness. Analyzed 6+ spending categories, with Bills ($14M) and Entertainment ($10M) identified as the top revenue contributors.
Online Retail Sales Performance Analysis
June 17, 2026 – Present
Utilized SQL to clean and aggregate sales data (revenue, orders, customers), ensuring accurate and structured datasets for analysis. Performed data analysis using Python (Pandas, NumPy) to identify sales trends, top-selling products, and seasonal patterns. Built an interactive Power BI dashboard showcasing key KPIs such as Revenue ($10.64M), Average Order Value (AOV), and top-performing products, improving data visibility. Generated actionable insights to identify high-performing products, peak sales periods, and country-wise revenue distribution, enabling data-driven business decisions.
Swiggy Instamart Financial & Reconciliation Analysis
January 1, 2021 – January 1, 2023
Managed and reconciled 50K+ financial transactions with automated daily data integration from GoFrugal to Zoho, ensuring up-to-date financial records. Performed structured data cleaning in Excel, improving financial data accuracy by ~30%. Built Power BI dashboards to monitor financial KPIs (revenue, outstanding balances, payment status), reducing manual reporting time by ~40%. Automated financial reporting workflows with real-time updates, driving ~25% efficiency improvement. Conducted variance and discrepancy analysis, reducing financial mismatches by ~20%. Generated insights on revenue patterns and payment cycles, enabling faster financial decision-making (~15% improvement). Ensured strong data integrity through continuous validation and reconciliation processes. Managed and validated vendor bank account details and payment records, ensuring accurate fund transfers, reduced payment errors, and timely vendor settlements.
Cultural Fit Analysis
The candidate's project experience, including both professional and academic projects, shows a consistent focus on data analysis and reporting, which aligns well with a Data Analyst role. The diversity of projects (financial reconciliation, credit card transactions, online retail sales) indicates adaptability and a broad interest in applying data skills across different domains. The explicit mention of 'My Strength (Data Analytics)' further reinforces their passion and alignment with data-centric roles.
Soft Skills & Operational Fit
The candidate demonstrates a results-driven approach, evidenced by quantifiable improvements in efficiency and accuracy in their projects. Their experience in automating financial reporting and managing large transaction datasets suggests strong organizational skills and attention to detail. The focus on delivering actionable insights aligns well with operational needs for data-driven decision-making.