Data Quality & Validation:- Validated ETL and analytics jobs, ensuring accurate data extraction, transformation, and loading (ETL) processes, with a focus on maintaining logical integrity and error-free execution.- Ensured data quality and consistency across all business layers, including source systems, data layers, Data Warehouse models, analytics, and reporting.Conducted large-scale data cleansing operations, enhancing data quality and reliability throughout the Data Warehouse.- Investigated and resolved data quality issues, implementing recovery ETL jobs to address missing or inconsistent data and aligning with source systems.Data Warehouse Modeling & Optimization:- Translated business needs into Data Warehouse models, defining dimension and fact models to support effective reporting and analytics.- Revamped DataStage jobs and optimized data models to enhance data processing performance and storage efficiency.Issue Resolution & Process Improvement:- Addressed and resolved conflicts between the Data Warehouse and source systems, identifying root causes and implementing long-term fixes.- Developed recovery jobs to ensure accurate data reporting and maintain data integrity across various reporting processes.Automation & Efficiency:- Developed Python scripts to automate validation processes, reducing manual effort and enhancing overall process efficiency.Dashboard & Reporting Validation:- Validated analytics and dashboard procedures, ensuring the correctness of logic, syntax, and output data.- Investigated and resolved reporting issues, ensuring data accuracy and alignment with business objectives.KPI Development & Trend Analysis:- Created KPIs to monitor data trends, assess the impact of issues, and recommend corrective actions to preemptively address potential problems.