Nishanth Sura

• Implemented agile development methodology, facilitating communication between cross-functional teams and serving as a liaison between business users and technical experts, resulting in a 15% increase in project efficiency.
• Utilized Python packages like NumPy, Matplotlib, and SciPy to generate graphical capacity planning reports, leading to informed decision-making and a 25% reduction in resource allocation errors.
• Conducted in-depth Data Analysis and Data Profiling, leveraging complex SQL queries on various source systems, including SQL Server, to derive valuable insights that drove a 30% improvement in data quality and decision-making accuracy.
• Seamlessly integrated SSIS with Python using script tasks, enhancing data analysis capabilities and manipulation techniques, resulting in a 20% reduction in data processing time.
• Utilized Pandas libraries in Python to import claims data, enabling various data analysis processes and insights generation, ultimately reducing data preparation time by 35%.
• Executed SQL queries for data analysis, modeling, and validation from relational databases, ensuring data accuracy and contributing to data-driven decision-making, resulting in a 20% increase in data-driven initiatives' success rate.

Implemented agile development methodology, facilitating communication between cross-functional teams and serving as a liaison between business users and technical experts, resulting in a 15% increase in project efficiency.
Utilized Python packages like NumPy, Matplotlib, and SciPy to generate graphical capacity planning reports, leading to informed decision-making and a 25% reduction in resource allocation errors.
Conducted in-depth data analysis and data profiling, leveraging complex SQL queries on various source systems, including SQL Server, to derive valuable insights that drove a 30% improvement in data quality and decision-making accuracy.
Seamlessly integrated SSIS with Python using script tasks, enhancing data analysis capabilities and manipulation techniques, resulting in a 20% reduction in data processing time.
Utilized Pandas libraries in Python to import claims data, enabling various data analysis processes and insights generation, ultimately reducing data preparation time by 35%.
Executed SQL queries for data analysis, modeling, and validation from relational databases, ensuring data accuracy and contributing to data-driven decision-making, resulting in a 20% increase in data-driven initiatives' success rate.
Demonstrated expertise in data cleansing, validation, and transformation, utilizing ETL methodologies to improve data quality and consistency for analysis and reporting purposes, reducing data errors by 25%.
Built and published customized interactive reports and dashboards using Tableau, ensuring accessibility and report scheduling for stakeholders, resulting in a 40% improvement in data accessibility and understanding.
Successfully extracted data from PostgreSQL databases, contributing to the creation of SQL queries, stored procedures, and common table expressions (CTEs) that played a pivotal role in multiple departmental reports, increasing report generation efficiency by 20%.
Engineered complex Tableau reports by employing functionalities like context filters, hierarchies, and LOD expressions, improving report accuracy by 20% and providing deeper insights into data trends.
Created business metric KPIs (Key Performance Indicators) to evaluate module-specific factors, enhancing performance assessment and enabling a 15% increase in targeted performance improvements.

Demonstrated expertise in data analysis, data warehousing, data migration, data cleaning, statistical modeling, and SQL programming, actively contributing to a 20% reduction in decision uncertainty.
Leveraged Python's statistical libraries to conduct hypothesis testing, significance testing, and confidence interval estimation, achieving a 95% confidence level in hypothesis testing.
Implemented sophisticated data cleaning pipelines in Python, leading to a 25% reduction in data errors and a 30% increase in data accuracy and consistency.
Utilized R for data acquisition and data integrity, encompassing datasets comparison and dataset schema checks, reducing data discrepancies by 15% through systematic data schema validation.
Crafted visually engaging charts, graphs, and plots using Matplotlib and Seaborn in Python, improving data comprehension by 25% and resulting in quicker decision-making.
Imported the customer data into Python using Pandas libraries and performed various data analysis, finding patterns in data which helped in key decisions for the company.
Played a key role in creating and maintaining database objects (tables, views, procedures, triggers, and functions) using SQL to optimize data management and enhance efficiency.
Implemented ETL methodology to support data extraction, transformations, and loading processes within a complex EDW using Informatica, reducing data processing time by 40% and improving data accuracy by 15%.
Deployed MongoDB's geospatial capabilities to execute spatial queries, providing valuable location-driven insights and increasing location-targeted marketing effectiveness by 10%.
Provided hands-on support to users in creating and modifying worksheets, data visualization dashboards, and Power BI reports, enhancing data analysis capabilities.
Developed Power BI data visualization using Cross tabs, Heat maps, Box and Whisker charts, Scatter Plots, Geographic maps, Pie Charts, Bar Charts, and Density charts.
Leveraged MS Excel for data rules spreadsheets and report preparation, including VLOOKUP, HLOOKUPS, pivot tables, Macros, and data points.
Utilized SSAS and SSIS to enhance data analysis capabilities, defining calculated measures, KPIs, perspectives, and processing large data volumes for reporting and analysis.