Data Quality & Validation:-Validated and monitored ETL and analytics jobs to ensure accurate and efficient data extraction, transformation, and loading (ETL) processes, prioritizing logical integrity and flawless execution.-Ensured data quality and consistency across all business layers, from source systems to Data Warehouse models, analytics, and reporting layers, supporting reliable decision-making.-Conducted extensive data cleansing operations, enhancing data quality and reliability within the Data Warehouse ecosystem.-Proactively investigated and resolved data quality issues, implementing recovery ETL jobs to correct missing or inconsistent data, and maintaining alignment with source systems.Data Warehouse Modeling & Optimization:-Translated complex business requirements into well-defined Data Warehouse models, developing both dimension and fact tables to drive effective reporting and analytics solutions.-Revamped and optimized DataStage jobs and data models, improving data processing speed and maximizing storage efficiency.Issue Resolution & Process Improvement:-Resolved conflicts between the Data Warehouse and source systems, identifying root causes and implementing strategic, long-term solutions to prevent recurrence.-Developed and implemented recovery jobs to maintain data integrity and support accurate reporting across diverse reporting processes.Automation & Efficiency:-Designed and developed Python scripts to automate data validation, significantly reducing manual tasks and increasing overall process efficiency.Dashboard & Reporting Validation:-Validated dashboards and analytics outputs to ensure accuracy in logic, syntax, and data alignment with business objectives.-Diagnosed and resolved reporting discrepancies, safeguarding data accuracy and ensuring alignment with key business metrics.

•Wrangling data by gathering it from flat files or scraping it from websites with APIs or HTML, and assessing it •Cleaning data to reach a clean and tidy dataset and then Saving the clean dataset into a database as required.•Explore data build intuition, find patterns, and get familiar with data•Drawing conclusions and making visualizations•Using spreadsheets for data analysis