Padmashree N

Automated Data File Loading Framework (SQL Server + Python)

• Designed and implemented SSIS packages to automate ingestion of Excel and CSV files into SQL Server, streamlining ETL workflows for healthcare data.
• Overcame Excel .xlsx compatibility limitations by developing a Python-based automation script that seamlessly converted Excel files into CSV format on user laptops, ensuring uninterrupted data loading.
• Integrated Python automation with SSIS workflows, reducing manual preprocessing effort by 70% and improving overall pipeline reliability.
• Established robust error handling, logging, and validation checks to guarantee data integrity during file ingestion.
• Delivered a hybrid solution combining SQL Server ETL capabilities with Python scripting, showcasing adaptability and cross-platform problem-solving.

Query Optimization & Index Housekeeping for Country-Specific Databases

• Conducted query performance tuning and index optimization on large country-specific SQL Server databases, reducing execution time of scheduled jobs significantly.
• Implemented housekeeping strategies (index rebuilds, statistics updates, removal of unused indexes) to maintain database health and improve query efficiency.
• Enhanced overall application performance by ensuring scheduled jobs executed faster and with reduced resource contention.
• Collaborated with application support teams to monitor workloads, identify bottlenecks, and apply targeted optimizations.
• Delivered measurable improvements in job completion times, contributing to smoother operations and improved SLA compliance.

New File Layout Configuration & Business Rule ETL Development

• Designed and implemented ETL tasks in SQL Server (SSIS) to process new file layout configurations, ensuring seamless integration of diverse data sources into the application.
• Developed business-specific rules in stored procedures to validate, transform, and standardize incoming data according to application requirements.
• Automated ingestion workflows to handle evolving file structures (CSV, Excel, custom layouts), reducing manual intervention and improving adaptability to client changes.
• Enhanced data integrity and consistency by embedding validation logic directly into ETL pipelines, ensuring compliance with healthcare business rules.
• Delivered a scalable solution that improved application performance and reduced onboarding time for new data sources.

Data Deduplication & Performance Optimization in SQL Server

• Analyzed last 3 production datasets to identify recurring data duplication issues impacting downstream systems under different business conditions.
• Designed and implemented a SQL Server view with filtering logic to ensure only distinct values were passed to downstream applications.
• Embedded business-specific rules into the view to handle conditional duplication scenarios, improving data accuracy and consistency.
• Optimized downstream performance by reducing redundant records, resulting in faster job execution and improved application reliability.
• Delivered a scalable solution that strengthened data integrity and enhanced overall ETL efficiency.

Multi-Sheet Excel Consolidation ETL for Application Loading

• Enhanced SSIS ETL workflows to process Excel files containing multiple sheets, consolidating all pages into a single SQL Server table before loading into the application.
• Implemented Foreach Loop Containers and dynamic sheet detection to automate ingestion of all sheets, eliminating manual effort in handling separate files.
• Designed staging and consolidation logic with business-specific rules to ensure data integrity, remove duplicates, and deliver clean, unified data to the application.
• Improved downstream application performance by providing a single consolidated dataset, reducing fragmentation and speeding up scheduled jobs.
• Automated validation and logging, which reduced manual intervention and saved significant user time in preparing and processing data files.

Application Support & Maintenance Responsibilities

• Incident Resolution: Analyzed and resolved application issues reported by users, including ETL failures, query performance bottlenecks, and data integrity problems.
• Root Cause Analysis: Investigated recurring issues by reviewing logs, monitoring SQL Server jobs, and analyzing production data to identify and fix underlying problems.
• Collaboration Across Teams: Worked closely with development teams (for code fixes), DBAs (for performance tuning and index maintenance), and business users (for requirement clarification and validation).
• Preventive Maintenance: Implemented housekeeping tasks such as index rebuilds, statistics updates, and monitoring of scheduled jobs to proactively prevent downtime.
• User Support: Provided guidance to business users on data-related issues, ensuring timely delivery of validated outputs (Excel/CSV files, consolidated reports).
• Change Management: Coordinated with multiple groups during deployments, ensuring smooth rollouts and minimal disruption to production systems.
• Performance Monitoring: Built dashboards and alerts to track job health, query execution, and ETL pipeline performance, enabling faster response to incidents.