Jignesh Patel

• Created Power BI dashboards with slicers, drill-through, and custom visuals. Applied complex DAX formulas to calculate weighted sales growth and territory market share, empowering sales leadership with interactive, real-time insights for decision-making • Used Python with Pandas, NumPy, Scipy for data wrangling, hypothesis testing, and regression diagnostics, enabling precise statistical analysis without ML or visualization. Cleaned and transformed data for robust insights across multiple sales territories • Utilized SQL with advanced window functions and CTEs for complex queries. Extracted and optimized data from AWS Athena, processing structured (CSV, Parquet) and semi-structured formats. Built ETL pipelines, accelerating data refresh by 30% • Performed descriptive and time series trend analysis plus cohort benchmarking to evaluate territory sales. Identified growth opportunities, boosting targeted campaigns’ effectiveness by 15% and highlighting under performing regions for strategic focus • Executed data validation and cleansing using AWS Glue ETL jobs, enhancing data integrity and reducing errors by 25%. Automated quality monitoring with AWS Lambda, ensuring reliable and continuous data pipelines throughout the 10-month project • Worked on Sales Territory Performance Analysis and Reporting, collaborating with Sales, Finance, and IT teams using Agile. Led requirement sessions, analyzed geographic sales performance, and helped improve regional revenue allocation by 18% through actionable insights

• Created Power BI dashboards with slicers, drill-through, and custom visuals. Applied complex DAX formulas to calculate weighted sales growth and territory market share, empowering sales leadership with interactive, real-time insights for decision-making • Used Python with Pandas, NumPy, Scipy for data wrangling, hypothesis testing, and regression diagnostics, enabling precise statistical analysis without ML or visualization. Cleaned and transformed data for robust insights across multiple sales territories • Utilized SQL with advanced window functions and CTEs for complex queries. Extracted and optimized data from AWS Athena, processing structured (CSV, Parquet) and semi-structured formats. Built ETL pipelines, accelerating data refresh by 30% • Performed descriptive and time series trend analysis plus cohort benchmarking to evaluate territory sales. Identified growth opportunities, boosting targeted campaigns’ effectiveness by 15% and highlighting under-performing regions for strategic focus • Executed data validation and cleansing using AWS Glue ETL jobs, enhancing data integrity and reducing errors by 25%. Automated quality monitoring with AWS Lambda, ensuring reliable and continuous data pipelines throughout the 10-month project • Worked on Sales Territory Performance Analysis and Reporting, collaborating with Sales, Finance, and IT teams using Agile. Led requirement sessions, analyzed geographic sales performance, and helped improve regional revenue allocation by 18% through actionable insights

• Used Python with NumPy and Pandas to clean and transform trade records. Automated computation of trade lag times and exception flags which reduced manual data handling time across teams by 38% • Built complex SQL queries for many data sources and optimized data pipelines using Azure Synapse and Azure Data Factory. Integrated structured datasets including trade logs, timestamps, and settlement records to support real-time exception tracking • Built interactive Tableau dashboards featuring filters, drill-down paths, and trend analytics. Used Level of Detail (LOD) expressions to isolate exceptions by asset class which improved SLA breach reporting speed by 46% • Performed time series analysis and root cause variance assessments across asset classes. Identified delay clusters in FX trades and revealed that 27% of all settlement exceptions were tied to booking stage inaccuracies • Leveraged R and the dplyr package to group exceptions by product type and geography. Applied summarization techniques to highlight recurring issues and helped reduce monthly unresolved exceptions by 19% across business lines • Designed and executed validation rules in Azure Data Factory including schema matching, null detection, and threshold filters. Ensured ETL pipeline consistency and improved end-to-end data quality with a 42% drop in reprocessing needs • Collected trade lifecycle data for the End-to-End Trade Lifecycle Performance and Exception Analytics project from front, middle, and back-office systems. Collaborated with trading, operations, and compliance teams in agile sprints to define reporting scope and requirements

• Supported SQL-based data extraction and transformation using Azure Data Factory. Helped validate inputs such as branch costs, transaction counts, and revenue figures, ensuring structured and accurate data for dashboard integration and reporting accuracy • Assisted in collecting branch-level financial and operational data for the efficiency dashboard project. Participated in agile meetings with regional teams to understand revenue, cost, and transaction flow processes used in reporting requirements • Helped build Tableau dashboards with filters and location-based visualizations. Used Level of Detail (LOD) calculations under guidance to display branch profitability insights, supporting regional managers in reviewing underperforming zones across the network