• Analyzed sales data across multiple product categories to identify trends, Distribution channels, customer purchasing behavior, and key drivers of sales growth, leading to a 18% increase in quarterly revenue.• Analyzed sales data using SQL, identifying key drivers of sales performance and contributing to a 10% increase in market share.• Designed and automated reporting processes through ETL techniques, reducing reporting time by 30%.• Developed and maintained automated dashboards using Power BI to monitor sales performance, inventory turnover, and market share, providing management with real-time insights• Conducted predictive analytics using Python, forecasting demand and helping to optimize inventory levels, reducing stockouts by 15% and minimizing overstock by 10%.• Collaborated with marketing and sales teams to design data-driven campaigns, leading to a 20% increase in promotional product sales.• Designed SQL queries to extract and analyze sales data, identifying slow-moving products and contributing to inventory clearance strategies.• Created weekly and monthly reports to track sales performance, distribution efficiency, and promotional effectiveness, presenting findings to the sales and marketing teams.• Worked with cross-functional teams to gather data requirements and ensure accurate data collection, improving the overall quality of business intelligence reports

• Collaborated with technicians to assess and highlight frequently used A/C and GEN materials, successfully reducing material costs by 20% by negotiating bulk purchasing agreements. • Developed Power BI dashboards that provided real-time insights into service performance, technician workload, and customer service trends, enabling better decision-making and operational planning. • Created comprehensive monthly reports on service trends, revenue breakdown, and technician performance, presenting findings to management for strategic planning• Conducted revenue analysis to pinpoint key drivers of revenue growth, with a focus on A/C supply services, which resulted in a 25% increase in overall revenue by optimizing product offerings and marketing strategies.• Analyzed sales data from multiple fashion collections, identifying seasonal trends, top-selling products, and consumer behavior patterns, contributing to a 15% increase in overall sales.• Developed and maintained automated Power BI dashboards to monitor sales performance, inventory levels, and customer demographics, providing real-time insights to the marketing and design teams.• Conducted predictive analytics using Python to forecast demand for upcoming fashion seasons, optimizing stock levels and reducing overstock by 20%.• Created SQL queries to extract and analyze sales and inventory data, identifying top-performing products and underperforming items for seasonal adjustments.

Analyzing data and preparing reports with data visualization tools (PowerBI, Think-cell, PowerPoint) to FCMB effectively communicates trends and patterns.• keeping track of the automated ETL processes for FCMB Corporate Banking• Enhancing FCMB Retail banking by providing different insight (Daily , Weekly, New customers and Monthly) analysis which shows different live activities about the different segments in the retail banking sector.• Developing data-driven decisions that promote corporate success, and derive insight from messy data utilizing technologies like SQL, Python, and Excel for analysis.• Assisting FCMB in discovering chances to investigate and improve their banking service.• Enhancing FCMB corporate banking zones by providing a deep dive analysis on each zones to give an insight on all in-flow and out-flow zones customer and transactions.Generated data spools from databases for use by the corporate strategy unit to drive actions• Migrated data using SSIS, linked servers and stored procedures· Spearheaded the development of the Python Training Manual, resulting in a 20% increase in employee proficiency in Python for data analysis and machine learning applications.· Coordinated efforts to produce the Excel Training Manual, resulting in a 10% reduction in errors across company operations and a 15% increase in Productivity and data accuracy.