Padmini M.

Analyzed large-scale financial datasets related to Cash Equities and Fixed Income Products using Python and SQL; applied statistical anomaly detection for accurate daily P&L reports intended for senior stakeholders. Designed and monitored data pipelines to facilitate seamless ingestion, transformation, and validation of over 500K records, ensuring high data integrity for daily reconciliation processes. Executed cross-regional data reconciliation between UK and US datasets by employing data modeling and validation techniques, thereby maintaining consistency and compliance. Utilized automation and machine learning tools to enhance P&L reconciliation, which led to a significant reduction in manual reporting time, while building scalable ETL workflows aligned with data quality governance standards. Collaborated with technology and operations teams to troubleshoot complex data pipeline challenges and provide analytical support during system migrations and upgrades.

Conducted extensive data quality assurance on billing transaction records exceeding 500K using SQL and Python, applying statistical validation techniques to maintain data integrity across diverse platforms. Engineered data cleaning and preprocessing pipelines to effectively manage large datasets, identifying and resolving critical data quality inconsistencies. Developed and automated analytical dashboards that tracked key performance indicators, enabling cross-functional teams to leverage data for informed decision-making. Identified gaps in data pipelines through exploratory data analysis and led initiatives for continuous improvement, implementing machine learning solutions for workflow optimization. Communicated complex trends and insights during cross-functional meetings, translating analytical findings into practical recommendations for a wide range of stakeholders.