• Spearheaded the implementation of Azure Data Factory and Databricks integration to engineer data pipelines, extracting data from 100+ blobs and mounting it into Delta Lake bronze tables, driving a remarkable 30% reduction in processing time.• Managed the loading of large datasets into the data warehouse using Informatica PowerCenter, optimizing for performance and storage efficiency.• Utilized Informatica PowerCenter’s advanced features, such as reusable transformations and mapplets, to streamline ETL processes and improve maintainability.• Automated ETL workflows and data integration tasks in Informatica PowerCenter to reduce manual intervention and operational overhead.• Collaborated with business analysts and stakeholders to gather requirements and translate them into effective ETL solutions using Informatica PowerCenter.• Utilized advanced SQL techniques, such as subqueries, common table expressions (CTEs), and window functions, to perform complex data transformations and analysis.• Managed and optimized large-scale database operations, ensuring high performance and reliability of data storage and retrieval.• Implemented data validation and quality checks within SQL scripts to maintain data integrity and accuracy.• Collaborated with data analysts and stakeholders to gather requirements and develop SQL solutions that meet business needs

• Implemented PySpark transformations on silver tables to optimize data transformations and loading refined data into gold tables for efficient BI reporting. • Utilized PySpark SQL for querying structured data and performing aggregations, joins, and window functions to derive actionable insights.• Adept at developing secure APIs for system and application integrations using protocols such as SOAP and REST, with additional experience in utilizing the MuleSoft Any point Platform for API management and integration.• Engineered a POC to revolutionize the data transfer architecture from Data Lake to Delta tables by implementing a staging pipeline. Orchestrated a seamless ingestion flow, resulting in a significant 40% improvement in data processing efficiency.• Implemented Snow pipes, automating the ingestion of transformed data from Azure into Snowflake for seamless and real-time data loading• Automated Cron jobs (ETL) with Glue Data crawlers, using scheduled and conditional triggers, improving job efficiency by 25% and enabling timely data loading to S3. Utilized Athena to analyze data for retrieving EPS statistics.• Facilitated data engineers in transforming the data loading pipeline from Redshift to Snowflake by designing UDFs on file formats (JSON, Parquet) from data Lake, resulting in a cost reduction.• Utilized SQL to create and automate regular and ad-hoc reports, improving data visibility for business stakeholders.• Developed interactive Power BI dashboards and reports, enabling real-time data visualization and insights for key stakeholders.