OM Core - open-source multidimensional modeling engine.

Job-ready FP&A & Financial Analytics portfolio—forecasting, variance analysis, KPI dashboards, and executive reporting (Python/SQL).

Read-only MCP server for FP&A & management reporting — governed metrics, financial statements and drill-down over a SQL semantic layer. Your own financials on your own warehouse, not market data. Open core of Précis. (metric engine · ClickHouse · OIDC · Docker)

Excel dashboard to identify overspending across departments and highlight areas requiring immediate cost control

经营管理导向财务分析看板：用 Python 将财务报表与业务明细转化为可离线运行的 FP&A 管理驾驶舱，含风险检测、杜邦分析、营运资本、利润中心与投资敏感性分析。

AI skills for CFO & Finance teams — connects GitHub Copilot and M365 Copilot to Dynamics 365 Business Performance Analytics via live DAX queries.

FP&A Analysis Toolkit

End-to-end financial analytics architecture integrating IFRS financial modeling, a PostgreSQL-based financial data warehouse, Python forecasting models, and Power BI executive dashboards.

Four-page Power BI financial performance dashboard built from the SQL case-study dataset with 21 DAX measures, executive KPIs, and budget tracking.

Plataforma de inteligência econômica que transforma indicadores oficiais em recomendações para revisão das premissas do Forecast.

Ten agentic-AI projects for corporate finance, healthcare RCM, FP&A, M&A, and treasury. By Sheharyar Monnoo.

FP&A / Cashflow Scenario Analytics

FP&A-style financial modeling and variance analysis using PostgreSQL. Includes revenue forecasting, gross margin analysis, and budget-to-actual performance reporting.

Budget vs actuals → driver-attribution variance commentary, not just numbers. Agentic AI for FP&A.

Personal website — FP&A + Bitcoin developer

Finance-oriented PostgreSQL case study transforming synthetic e-commerce data into revenue, margin, discount, segment, and budget variance insights.

Finance automation portfolio for month-end close, reconciliations, ERP data quality, VAT/EFD controls, MCP servers, and audit-ready reporting.

FP&A-style budget vs actuals variance analysis using Python, SQL and Power BI, with management commentary and business recommendations.

Driver-based rolling revenue forecast on dbt and DuckDB with base, upside, and downside scenarios and a leak-free backtest that measures accuracy honestly: 3.61 percent MAPE, a 42 percent improvement over a seasonal-naive baseline, guarded by dbt tests.

Developed a corporate finance dashboard analyzing $127.9M revenue, budget variance, profitability trends, COGS behavior, and segment-level KPIs using MySQL, Power BI and Excel. Performed budget vs actual analysis, variance reporting, MoM revenue tracking, and executive-level FP&A insights.