Ashwin S

Project : Automated Ingestion and Transformation using Snowflake for Investment Banking

Domain : BFSI

Environment : AWS S3,Snowflake DB

• Implementation of scalable Snowflake data warehousing solutions, enhancing efficiency in data management processes.
• Demonstrated proficiency in developing a comprehensive array of Snowflake objects, encompassing warehouses, schemas, databases, tables, views, pipes, stages ensuring comprehensive data governance.
• Managed structured data migration with meticulous attention to detail, overseeing stages, copy operations, data transformations, error handling protocols, and employing a wide range of copy options for enhanced precision.
• Implemented incremental loads to enhance performance of large scale data loads.
• Loaded unstructured data efficiently using VARIANT datatype, flattening methods, and innovative techniques to improve data accessibility.
• Create and enhance sophisticated SQL queries to bolster data analysis and elevate business intelligence capabilities.
• Developed Snowflake procedures, functions, and User Defined Table Functions (UDTF), automating operations and improving capabilities for streamlined data manipulation.

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Project : Financial Data Warehouse Modernization Using Snowflake, DBT.

Domain : BFSI

Environment : DBT, Snowflake DB

• Designed and developed modular dbt models for staging, dimension, and fact tables using Snowflake as the backend.
• Implemented incremental models with strategies to optimize large-scale data loads. Utilized dbt tests and snapshots to ensure data quality and track historical changes.
• Created reusable macros and utilized Jinja templating to streamline development, while maintaining clear model-level documentation in YAML.
• Handled version control using GitHub - created branches, committed changes, raised Pull Requests (PRs), and merged into main.
• Managed multiple environments using dbt Cloud. scheduled production runs with email alerting.
• Collaborated with cross-functional teams to gather requirements, improve pipeline performance, and deliver clean, trusted data.

• Investigated job failures, performed SQL-based troubleshooting, and ensured timely resolution.
• Handled incident tickets using ServiceNow, ensured SLA compliance.
• Coordinated with L2/L3 teams for complex issues and deployment support.
• Monitored daily data pipelines, batch jobs, and application health in production systems.
• Prepared runbooks and documented known issues for future reference.