Yohannes Legesse

• Led the design and execution of complex SQL queries in Azure SQL Database and Azure Synapse Analytics, improving reporting efficiency by 35% and reducing data retrieval time by 25%.
• Automated data transformation processes using Python (Pandas, NumPy), enhancing data accuracy and completeness, resulting in a 20% reduction in data preparation time.
• Developed and optimized interactive Power BI dashboards, streamlining KPI visualization and enabling stakeholders to drive faster, data-driven decisions, leading to a 15% improvement in operational efficiency.
• Executed advanced statistical analyses to identify key trends, influencing business strategies that resulted in a 10% increase in forecast accuracy.
• Streamlined manual reporting tasks by automating data extraction and reporting pipelines with Python scripts and Azure Data Factory, reducing report generation time by 40%.
• Integrated and consolidated data from Azure Data Lake, SQL databases, and external APIs, providing a unified data platform that improved cross-functional reporting accuracy by 25%.
• Collaborated with business and technical teams to define key metrics and deliver customized Power BI reports, improving alignment with strategic objectives and driving a 12% increase in stakeholder satisfaction.
• Directed data quality assurance processes by creating automated Python-based validation scripts, decreasing report discrepancies by 30% and improving data reliability.
• Analyzed historical business data using Azure Synapse Analytics, uncovering actionable insights that led to a 15% improvement in operational performance across marketing and sales teams.

Engineered complex SQL queries views,and stored procedures in SQL Server and Azure SQL Database to optimize data extraction, aggregation, and reporting, reducing query execution time by 30% and enhancing reporting performance.

• Optimized advanced SQL joins, window functions, and subqueries to efficiently process large-scale datasets, improving analytical accuracy and reducing manual reporting effort by 25%.
• Developed and automated data transformation processes using Python (Pandas, NumPy), improving data consistency and reducing errors by 30% for downstream analysis, supporting data-driven decision-making.
• Utilized advanced SQL techniques to aggregate, clean, and validate datasets, reducing discrepancies by 20% and improving the accuracy and reliability of business reports.
• Built and optimized SQL queries to support real-time business intelligence and reporting, leveraging Power BI and SQL Server to reduce report delivery time by 35% and enhance stakeholder access to key metrics.
• Streamlined data extraction and transformation workflows through Python-based scripts, reducing time spent on manual data processing by 40% while ensuring high data quality.
• Collaborated closely with data engineers to design and implement efficient data workflows, ensuring seamless integration between SQL databases and data processing pipelines, improving operational performance.

• Conducted data wrangling and cleansing using Python (Pandas, NumPy) and SQL to preprocess large healthcare datasets, including handling missing data, outliers, and inconsistencies, improving data quality and consistency by 15% for downstream analysis and reporting.
• Performed statistical analysis on clinical outcomes and patient satisfaction data using Python (SciPy, Statsmodels), identifying patterns and correlations to generate actionable insights, which boosted patient satisfaction by 8% through improved resource management and operational changes.
• Developed dynamic dashboards and reports using Tableau and SQL, combining clinical, operational, and financial data to deliver actionable insights, enabling hospital administrators to monitor trends and improve reporting efficiency by 20%.
• Automated routine clinical reporting processes by integrating Python scripts with SQL queries to generate reports on bed utilization, patient turnover, and discharge planning, resulting in a 30% reduction in report generation time and allowing staff to focus on more strategic tasks.
• Analyzed electronic health records (EHR) data to uncover trends and insights related to patient care, leveraging Python for data manipulation and SQL for complex queries, which supported a 12% increase in treatment efficacy through better decision-making.
• Collaborated with clinical and operational teams to review patient care data trends and ensure alignment with strategic goals, extracting actionable insights from SQL databases and Python-based analysis to contribute to a 15% increase in treatment compliance across patient populations.