Data Analyst: Clean, Query, Visualize

I have several raw datasets from different business systems that need to be turned into a single, reliable source of truth before we explore the story hidden inside. For the cleaning phase I’ll rely on both Python (think pandas, NumPy, Jupyter notebooks) and advanced Excel techniques such as Power Query and dynamic arrays—whichever makes the job faster and more transparent for later hand-off. All tables ultimately live in MySQL, so you’ll be writing efficient joins, sub-queries, and window functions directly against that database to create analysis-ready views. Performance matters; I want the queries optimized and commented so they can be reused by the in-house tech team. Once the data is shaped, the next step is to craft an interactive, executive-level dashboard in Power BI. It should surface our key performance metrics with drill-through, tooltips, and clean, brand-aligned visuals that leadership can refresh on demand. Beyond the visuals, I’m counting on you to extract clear, actionable insights—patterns, anomalies, and trend explanations—and write a concise summary that decision-makers can act on without digging through the raw numbers. Deliverables • Cleaned, merged dataset (CSV or MySQL table) • Well-documented MySQL query scripts • Power BI (.pbix) file plus a PDF snapshot of the final report • Insight summary document highlighting trends, root causes, and recommended actions I’ll be available to answer domain questions quickly so we keep momentum. If this blend of Python, Excel, MySQL, and Power BI feels second nature to you, let’s get started.