Joy Chakraborty

Finance and Analytics professional with 6 years of experience across Financial Analysis, FP&A, Business Intelligence, reporting, and process improvement. Experienced in transforming financial and operational data into actionable insights through Power BI, Advanced Excel, SQL, and reporting automation. Strong understanding of business processes across financial reporting, forecasting, performance analysis, and Order-to-Cash/finance workflows, with a growing focus on Python, AI-enabled analytics, and intelligent automation. Currently building expertise at the intersection of Finance, Data, AI, and enterprise system design to develop scalable solutions that improve decision-making and operational efficiency.

Financial Analyst | FP&A | Finance Analytics | Power BI | Advanced Excel | SQL | Automation | AI for Finance | Business Intelligence

Tableau | Forecasting | Business Intelligence | Data Analysis

• Developed an interactive Tableau forecasting dashboard providing revenue visibility across 3 business dimensions — department, region, and category.
• Built 10+ analytical measures covering revenue, forecast, historical performance, variance, growth, and contribution.
• Enabled comparison of quarterly forecasts against year-over-year actual performance to identify revenue trends, performance gaps, and emerging opportunities.
• Designed a 4-level analytical drill-down hierarchy — company → region → department → category — to investigate underlying revenue drivers.
• Developed interactive KPI views enabling stakeholders to identify top-performing and underperforming regions, departments, and categories.
• Structured forecasting outputs around 3 core analytical perspectives — forecast, historical performance, and variance — to support quarterly business reviews and revenue planning.

3 business dimensions | 10+ measures | 4 drill-down levels | 3 analytical perspectives

Power BI | Advanced Excel | Financial Analysis | FP&A

• Migrated recurring financial reporting from Excel to Power BI, consolidating 7+ management and financial reporting views into interactive dashboards for improved performance visibility.
• Developed 5+ KPI perspectives covering financial health, productivity, expenses, profitability, and business performance for management review.
• Built actual-vs-forecast and variance-analysis views enabling stakeholders to evaluate MoM, YoY, and forecast performance across multiple business dimensions.
• Developed interactive dashboards incorporating 10+ financial and operational measures to support management reporting and performance monitoring.
• Automated recurring data preparation and reporting workflows using Advanced Excel and Power BI, reducing dependency on repetitive manual reporting activities.
• Structured financial analysis across multiple business dimensions, enabling drill-down from overall performance to business-unit and category-level drivers.
• Transformed complex accounting and operational datasets into decision-ready management reports supporting financial health, productivity, and performance analysis.

7+ reporting views | 5+ KPI perspectives | 10+ measures | 3 comparison perspectives

Python | SQL | Power BI | DAX | GenAI | FP&A

• Built an end-to-end FP&A analytics platform integrating 36 months of AR, AP, revenue, expense, cash-flow, and working-capital data into a unified finance model.
• Developed SQL-based finance data models across 10+ interconnected finance tables covering customers, vendors, invoices, payments, expenses, bank transactions, and cash movements.
• Developed a 13-week rolling cash-flow forecasting model incorporating expected collections, vendor payments, operating expenses, and other cash movements.
• Implemented 3 scenario models — Base, Optimistic, and Downside — to evaluate the impact of collection delays, expense changes, and payment obligations on projected liquidity.
• Built a Power BI CFO dashboard with 10+ executive KPIs, including cash balance, runway, DSO, DPO, AR/AP aging, cash conversion cycle, revenue, EBITDA, and forecast variance.
• Developed customer and vendor risk scoring using 5+ financial indicators, including overdue exposure, payment delays, credit utilization, and historical payment behavior.
• Created 5 analytical dashboard views covering Executive Health, Cash Flow, AR Intelligence, AP Intelligence, and Working Capital.
• Designed an AI Finance Copilot supporting 10+ predefined finance query types, including variance analysis, cash-flow explanations, AR risk analysis, AP obligations, and executive summaries.

36 months | 10+ tables | 13-week forecast | 3 scenarios | 10+ KPIs | 5+ risk indicators | 5 dashboard views | 10+ AI use cases

Python | SQL | Power BI | AI | Finance Analytics

• Designed an end-to-end finance analytics platform covering 3 core finance value streams — Order-to-Cash (O2C), Procure-to-Pay (P2P), and Record-to-Report (R2R) across customer, product, sales, purchasing, and finance operations.
• Built a scalable synthetic finance dataset covering 5,000+ customers, 1,000+ vendors, 500+ products, and 100,000+ transactional records for enterprise finance analytics.
• Developed a modular data architecture spanning 15+ master and transaction entities, using reusable keys and standardized relationships to support scalable analytics.
• Generated 36+ months of historical financial data to enable trend analysis, variance analysis, forecasting, scenario planning, and management reporting.
• Designed 20+ finance KPIs across revenue, profitability, expenses, working capital, AR, AP, cash flow, and operational performance.
• Implemented configuration-driven and reusable data-generation components supporting 10+ configurable business parameters without modifying core business logic.
• Designed an AI-enabled finance intelligence layer for automated variance explanations, financial risk identification, executive summaries, and management recommendations.

5K+ customers | 1K+ vendors | 500+ products | 100K+ transactions | 36 months | 15+ entities | 20+ KPIs | 3 finance processes