• Conducted data analysis using Matplotlib and seaborn libraries to visualize data effectively.• Implemented Spark with Scala and utilized Data frames and Spark SQL API for accelerated data processing.• Established data and reporting infrastructure using Power BI and SQL from scratch.• Developed large-scale data structures and pipelines using SQL and Python to standardize and organize data for insights and reporting.• Conducted feature engineering, model training, and comparison, and back-testing using Python (Pandas, Numpy, and Scipy).• Designed ETL processes for loading prospects into data marts and applied complex marketing segmentation for targeted mailing.• Created and maintained Logical Data Model documentation, including entities, attributes, relationships, keys, and business rules.• Developed interactive and informative Power BI reports using advanced DAX features.• Conducted Data visualization using Snowflake and PostgreSQL for insightful reporting.• Developed SQL scripts for data visualization and reporting.• Implemented data warehousing concepts and techniques using SQL.• Assessed existing data, identified gaps, and worked with Data Strategy and business partners to devise solutions.• Wrote ETL processes, designed database systems, and developed tools for real-time and offline analytics processing.• Designed and managed ETL processes using SQL.• Built data-cleaning ETL pipelines to process raw text and extract information for market analysis data (Python, PostgreSQL).

• Conducted Data Analysis to identify Data Sets, Source Data, Source Metadata, Data Definitions, and Data Validation.• Created various profiles using Informatica Data Explorer (IDE) & Informatica Data Quality (IDQ) and shared them with business analysts for analysis.• Worked on STAR schema for detailed data marts and Plan data marts involving confirmed dimensions.• Performed Validation, Dimensional Analysis, Statistical Analysis, and Data Quality Validation in Data Migration.• Generated portfolio analysis and P&L reports by performing risk analysis and risk management.• Created multiple dashboards using Power BI - PowerPivot & Power Query tools.• Documented logical, physical, relational, and dimensional data models.• Involved in the implementation of MDM concept with upstream and downstream integration of different applications.• Developed multiple custom SQL queries in Teradata SQL Workbench for Tableau dashboards.• Provided Production support to Tableau users and wrote Custom SQL to support business requirements.• Delivered Enterprise Data Governance, Data Analyst, Metadata, and ETL Informatica solutions.• Assisted the ETL team to define Source to Target Mappings.• Troubleshot ETL mappings and addressed production issues like performance tuning.• Translated business requirements into data requirements across different systems.• Conducted unit testing on transformation rules to ensure data integrity.• Conducted daily data quality checks and data profiling for accurate reporting and analysis.• Developed SQL and PL/SQL queries on data staging tables and data warehouse tables.

• Developed SAS macro sets and programs for verification processes and model implementation.• Generated Data Models using Python and Azure Machine Learning for predictive applications.• Predicted donor acceptance for charity organizations using Auto Machine Learning tools in Python.• Participated in the design and development of a consolidated data warehouse.• Applied predictive analysis techniques to analyze customer behavior.• Applied Quality Management System Software for defining and implementing data quality.• Analyzed customer master data for prospective business identification.• Designed, developed, and implemented data quality validation.• Utilized Python libraries like NumPy, Pandas, SciPy, Matplotlib, and Scikit-learn for extensive hypothesis testing, Statistical Modeling, Data Mining, and Machine Learning.• Performed model tuning using techniques such as Grid Search and K-Fold Cross Validation.• Implemented unsupervised learning algorithms including Clustering and Principal Component Analysis.• Worked on data profiling & various data quality rules development using Informatica Data Quality.• Designed and built data warehousing and reporting platforms.• Developed and maintained MySQL database structures.• Maintained and enhanced existing SAS reporting procedures.• Performed Data analysis, statistical analysis, and generated reports using SAS Tools.• Gathered requirements and modeled the data warehouse.• Designed and developed logical and physical data models to support Data Marts and Data Warehouse.• Implemented business intelligence dashboards using Tableau.• Conduct thorough data analysis and preparation, leveraging historical models to uncover patterns within datasets.

• Assisted senior business analysts in defining project scope and business rules.• Gathered business requirements and performed gap analysis.• Created context and workflow models, information and business rule models, and Use Cases.• Documented requirements and transformed them into functional and technical requirement specifications.• Developed prototypes of new information processing applications.• Assisted in developing applications and process mapping.• Worked as a Tester during the test phase and maintained an Issue Track Log.• Linked the SQL server with Tableau to develop visualizations depicting room utilization, leading to the identification of underutilized areas.• Reorganized and refined patient data spanning five years, conducting Descriptive Analytics and presenting visualizations in Tableau. This initiative boosted prime-time operation room utilization by 41%.• Segmented the dataset into test and train data at a ratio of 7:3, employing decision trees in Python with the Scikit-learn package to assess the accuracy of selected metrics.• Implemented random forest as an alternative model to decision trees, resulting in a 5% improvement in model accuracy.• Imported SQL server work results into Python, leveraging NumPy and Pandas to cleanse datasets and conducting Exploratory Data Analysis (EDA) with the matplotlib package.• Established connectivity between SQL server and R Studio via ODBC connection, visualizing patient data within the R Studio environment.• Conducted data analysis, statistical analysis, and generated reports, listings, and graphs using R Studio.