Praveen Kumar

Developed and managed high-volume ELT pipelines in Snowflake, ingesting and transforming critical mortgage servicing data from core systems including DB2 for Mr. Cooper.

Architected complex data models using star and snowflake schemas for mortgage entities and financial data, supporting robust analytical frameworks and regulatory reporting.

Engineered near real-time incremental data loads using Snowflake Streams, Tasks, and Snowpipe, significantly reducing data latency for critical business updates across multiple clients.

Optimized Snowflake query performance and storage efficiency through clustering keys, materialized views, and strategic partitioning, reducing compute costs and improving data retrieval speeds.

Ensured stringent data quality by implementing comprehensive validation rules, reconciliation logic, and audit checkpoints across all data layers, maintaining data integrity for financial and insurance data.

Enabled compliant regulatory reporting by developing traceable, analytics-ready datasets with complete data lineage and comprehensive compliance documentation for financial and insurance clients.

Led the migration of critical financial and regulatory data from on-premise databases to Snowflake, ensuring seamless transition and data integrity for Citigroup.

Collaborated cross-functionally with stakeholders across Mortgage Servicing, Risk, Compliance, and Finance to translate complex business requirements into scalable Snowflake data solutions.

Engineered and optimized ELT/ETL pipelines leveraging Snowflake's Snowpipe, Streams, and Tasks to process high-volume logistics data, significantly enhancing real-time data availability.

Integrated diverse large-scale datasets from transportation, warehouse management, and ERP systems into Snowflake, establishing a centralized, analytics-ready data repository.

Optimized Snowflake query performance and warehouse resource utilization, resulting in significant cost savings and accelerated data processing speeds.

Implemented robust data quality checks, validation rules, and exception handling mechanisms within pipelines, ensuring data accuracy and reliability for critical logistics operations.

Automated daily logistics reporting and real-time alert generation using Snowflake Tasks and advanced orchestration tools, improving operational visibility and response times.

Authored comprehensive technical documentation for data models, pipeline architectures, and business data flows, facilitating knowledge transfer and compliance.