Finance professional (MBA - Finance ) with hands-on experience spanning core accounting, financial reporting, and end-to-end financial analytics. Transitioning from historical record-keeping into forward-looking corporate finance: rolling forecasts, driver-based 3-statement model, OpEx budget variance and decision-support modeling. I leverage SQL Server, Power BI, Excel and Python to transform raw transactional data into actionable financial decision frameworks.
Everything below I built end to end, from raw data through to a recommendation. Datasets are synthetic where real financials would be confidential, and each repo says so.
Python, SQL Server, Excel, Power BI
The problem. A company closes the year 4% over budget on Rs 3,758 Cr of operating expenses. That looks tolerable, so nobody digs further.
What I did. Built the pipeline across 36 months, 13 departments and 14 cost categories: dataset generated in Python with the faults real ERP extracts carry, cleaned and analysed in SQL Server, reported through an Excel variance bridge and a Power BI dashboard.
What it found. The 4% was a net figure. Rs 239.7 Cr overspent sat against Rs 88.9 Cr underspent, so absolute variance was 8.7% of budget, not 4%. The company had an allocation problem, not an overspending one. Two of the largest variances also turned out to be single events rather than trends, caught by comparing each cost line to its own monthly average instead of sorting by size.
SQL Server, Excel, Power BI
The problem. A retail apparel chain discounts to move stock and wants to know where discounting stops paying for itself.
What I did. Worked through 9,600 order lines at transaction level in SQL, then built a discount scenario model and a seasonal revenue forecast in Excel.
What it found. Gross margin holds around 45% at full price and falls to 9.5% once discounts pass 30%. Footwear is the second largest category by revenue but the lowest margin of the majors.
Excel
The problem. Historical statements tell you what happened. They do not tell you what happens if growth slows or margins compress.
What I did. Built a linked income statement, balance sheet and cash flow for Mphasis Ltd, extended into a three year driver based forecast.
What it gives you. A Base, Bull and Bear toggle running off assumptions for revenue growth, working capital and margins, so the three statements move together when a driver changes.
SQL Server for cleaning and analysis: CTEs, window functions, joins across a star schema. Power BI for dashboards, including the DAX behind them. Excel for financial models, variance bridges and scenario work. Python with pandas and numpy, mainly for data generation and preparation.
Working through Power BI financial reporting and DAX in more depth. Looking for an FP&A analyst, business finance analyst role. Open to relocating.
LinkedIn: gokul-r-nair