Finance Data Cleaning & Dashboard

I’m working with a raw financial dataset and I need it taken from “messy” to “decision-ready.” The core goal is to surface meaningful trends, so every step—from preparation through visualization—must support that objective. You’ll start by importing the data into Excel or a SQL environment and normalizing the fields so they’re analysis-ready. Once the structure is consistent, feel free to apply Python (Pandas, NumPy, matplotlib/seaborn) for deeper profiling and any additional quality checks you find useful. When the numbers line up, shift the focus to insight generation: uncover the key trend lines, seasonality patterns, or shifts that matter to stakeholders. The polished story should appear in Power BI: an interactive dashboard with clear metrics, drill-downs, and a few well-chosen charts that spotlight those trends at a glance. Wrap up with a concise written summary—no more than a page—highlighting what was discovered and how it could influence future decisions. Deliverables: • Normalized, clean financial dataset (Excel or CSV + SQL scripts) • Reproducible Python notebook or script used for analysis • Interactive Power BI dashboard file (.pbix) and link (if published) • Short executive summary of findings Everything should be logically organized, documented, and handed over by the agreed deadline so I can move straight into presentation mode.