vigneshwaran k

<p>Designed and developed Snowflake Data Cloud solutions for Banking and Financial Services clients, configuring multi-cluster virtual warehouses with workload isolation, auto-suspend, and auto-resume policies to support high-concurrency analytics and reporting — improving warehouse utilization by around 30%.<br>
Built end-to-end ELT pipelines using Snowflake Tasks and Streams (CDC) for automated, event-driven incremental data ingestion and transformation of financial transaction data — reducing manual data processing effort by approximately 60%.<br>
Developed and maintained dbt models on Snowflake across staging, intermediate, and mart layers — writing dbt generic and singular tests for data quality validation, dbt snapshots for SCD Type-2 slowly changing dimension tracking, and dbt macros for reusable logic — improving overall data reliability by around 40%.<br>
Configured Snowpipe for serverless, continuous data ingestion from AWS S3 into Snowflake — reducing data load latency by approximately 50% compared to scheduled batch loads and enabling more timely downstream analytics.<br>
Improved Snowflake query performance by designing clustering keys on financial fact tables, analyzing query profiles to identify pruning inefficiencies, and enabling result cache — achieving around 35% reduction in average query execution time.<br>
Enforced enterprise data governance and security by implementing Dynamic Data Masking for PII and sensitive financial fields, Row-Level Security (RLS) for client-level data segregation, Column-Level Security, and Role-Based Access Control (RBAC) in alignment with data privacy policies.<br>
Participated in the migration of legacy on-premise data pipelines to Snowflake — involved in schema design, historical data loading, data validation, and UAT support during the cutover phase, contributing to on-time project delivery.<br>
Integrated Snowflake with AWS S3 and IAM for secure, automated data ingestion workflows — loading structured and semi-structured data (JSON, Parquet, CSV) from cloud storage into Snowflake staging tables using external stages.<br>
Leveraged Snowflake Zero-Copy Cloning to provision development and testing environments instantly without duplicating storage — reducing environment setup time by around 70% — and used Time Travel for point-in-time data recovery.<br>
Supported Snowflake Resource Monitor configuration and reviewed warehouse credit usage reports to identify high-consumption queries and assist in cost management — contributing to an estimated 25-30% reduction in monthly warehouse credit spend.</p>

<p>Designed Snowflake data warehouse schemas including Star Schema and Snowflake Schema for multiple financial services clients — building fact and dimension tables optimized for high-performance analytical queries and BI reporting.<br>
Built and maintained data transformation models on Snowflake across staging, intermediate, and mart layers — implementing data quality checks and maintaining source documentation and freshness validations, reducing data quality incidents by around 40%.<br>
Developed Azure Data Factory (ADF) pipelines to ingest structured and semi-structured data from Azure Blob Storage into Snowflake — configuring Copy Activity, Linked Services, and parameterized pipeline templates for reusable, scalable data workflows and cutting development time by approximately 30%.<br>
Worked with Snowflake Streams for change data capture (CDC) on financial source tables and configured Snowflake Tasks for scheduled incremental processing — replacing manual full-load batch jobs and reducing overall pipeline execution time by around 50%.<br>
Wrote Snowflake Stored Procedures in SQL for multi-step data transformation logic, pipeline workflow control, and exception handling — improving pipeline reliability and reducing the need for manual intervention.<br>
Tuned Snowflake query performance by analyzing query profiles, applying clustering keys, and right-sizing warehouse configurations — achieving approximately 35% improvement in query scan efficiency on high-volume financial tables.<br>
Set up ADF integration runtimes, schedule and event-based triggers, and parameterized pipeline templates to automate and standardize data ingestion across multiple financial source systems.</p>

<p>Worked with Snowflake as a cloud data warehouse for storing and querying structured and semi-structured data (JSON, CSV, Parquet) to support data engineering pipeline and analytics requirements.<br>
Built and configured Azure Data Factory (ADF) pipelines using Copy Activity, Data Flows, Linked Services, and scheduled triggers to automate data ingestion and transformation workflows into Snowflake.<br>
Deployed and managed containerized application environments using Docker and Kubernetes on GCP — maintaining CI/CD pipeline stability and supporting 99.9% infrastructure availability.<br>
Supported infrastructure and application monitoring using Nagios, New Relic, and Kibana to track system health and proactively maintain service uptime commitments.<br>
Performed root cause analysis (RCA) on production pipeline failures and assisted in implementing preventive fixes and monitoring improvements to reduce recurring incidents.</p>

<p>Maintained availability of production applications hosted on DC and GCP servers, managing incidents and escalations to support uptime SLAs for enterprise clients.<br>
Collaborated with the application team on DDL and DML deployments and assisted in troubleshooting SQL query performance issues to improve database response times.<br>
Configured MySQL Master-Slave replication for database high availability and managed automated backup and recovery processes using InnoDB XtraBackup.<br>
Supported CI/CD pipeline monitoring and maintained infrastructure stability across development and production environments using Jenkins and GitLab CI.</p>