Vijay Rodrigues

• Developed comprehensive documentation for the Snowflake environment, enhancing knowledge sharing among over 10 junior and mid-level team members.
• Engaged with stakeholders to validate 30+ tables, schema, and data post-migration from on-prem SQL Server to Snowflake, ensuring data integrity and system performance.
• Initiated the conversion of existing SQL Server scripts to the Snowflake environment, optimizing queries for performance and compatibility.
• Initiating work with Databricks to integrate multiple data sources for ETL, with plans to utilize the processed data for Qlik Sense dashboarding to enhance data visualization and reporting.

Developed comprehensive documentation for the Snowflake environment, enhancing knowledge sharing among over 10 junior and mid-level team members.
Engaged with stakeholders to validate 30+ tables, schema, and data post-migration from on-prem SQL Server to Snowflake, ensuring data integrity and system performance.
Initiated the conversion of existing SQL Server scripts to the Snowflake environment, optimizing queries for performance and compatibility.
Initiating work with Databricks to integrate multiple data sources for ETL, with plans to utilize the processed data for Qlik Sense dashboarding to enhance data visualization and reporting.

• Improved reporting efficiency by converting more than 10 Qlik Scripts to SQL Server, enhancing workflows and standardizing data handling for Actuarial Property and Casualty (P&C) tasks, thus improving overall reporting effectiveness for the P&C business segment.
• Led the migration of SSIS packages for 6 data sources from SQL Server (on-prem) to PySpark, reducing data load time by 25% and increasing flexibility by minimizing reliance on licensed platforms.
• Maintained and optimized existing SSIS packages, developing new ones and fine-tuning SQL queries to improve execution speed by 15% and overall system performance.
• Developed and managed an automated Python reconciliation framework, increasing report generation efficiency by 90% and saving 4 hours of manual effort by automating data validation, testing, and integrity processes.
• Developed an automated reconciliation framework for the PCW data source, using PySpark to compare data between the Actuarial Server, Oracle, and AIP, identifying mismatches across all sources and improving data accuracy by 40%.
• Corrected premium transactions from ACE/CHUBB by mapping them to the right business lines in PySpark for Casualty, extracting and transforming over 15 years of historical data from Azure SQL, processing 10 – 20 million records, and eliminating manual data extraction.
• Implemented multithreading techniques to parallelize data extraction from Azure SQL, reducing processing time from 8 hours to 3 hours, improving overall efficiency by approximately 60%.

• Directed the biweekly migration of over 2 billion records from structured data sources to an on-prem Data Warehouse using SSIS, building data pipelines and a reliable repository for ANZ actuaries to ensure compliance and robust platform operations.
• Coordinated with business users and stakeholders to gather detailed requirements while collaborating with DBA's to streamline post-ingestion processes and ensure smooth project implementation.
• Orchestrated the transition of SAS scripts to R programming using RODBC. This migration substantially improved data management and accessibility, facilitating the transition of both technical and non-technical stakeholders from North America region, towards R for future utilization.
• Engineered a Python automation script to generate personalized Price Monitoring Report templates across various business lines for North America region reducing manual tasks and saving 12 to 14 hours per report generation cycle. This resulted in a yearly time-saving of approximately 168 hours.
• Utilized GitHub and JIRA for project documentation, improving project scope efficiency by 50%. Acted as a Subject-Matter Expert (SME) during requirements gathering for the Clarity project.

• Implemented Python automation to streamline the preparation of RFPs (Request for Proposals), resulting in a significant reduction of 3 hours in manual work.
• Successfully implemented a streamlined process change workflow and process improvements, resulting in a positive turnaround time and saving approximately 2 hours compared to previous methods.
• Served as a key intermediary between Account Managers and Retailers, facilitating effective communication and gathering essential business requirements for the tender process.

• Conducted thorough profile gathering and screening processes, utilizing referrals and several job boards, to ensure the submission of high-quality candidate profiles to the Client Manager.
• Gathered and translated business requirements from hiring managers and clients into actionable talent acquisition strategies.
• Provided support to Fortune 500 clients across 5 domains including Banking, Automotive, Retail, E-Commerce, and Healthcare, actively collecting market intelligence on relevant job positions.

Improved reporting efficiency by converting more than 10 QLIK scripts to SQL Server, enhancing workflows and standardizing data handling for Actuarial Property and Casualty (P&C) tasks, thus improving overall reporting effectiveness for the P&C business segment.
Led the migration of SSIS packages for 6 data sources from SQL Server (on-prem) to PySpark, reducing data load time by 25% and increasing flexibility by minimizing reliance on licensed platforms.
Maintained and optimized existing SSIS packages, developing new ones and fine-tuning SQL queries to improve execution speed by 15% and overall system performance.
Developed and managed an automated Python reconciliation framework, increasing report generation efficiency by 90% and saving 4 hours of manual effort by automating data validation, testing, and integrity processes.
Developed an automated reconciliation framework for the PCW (Premiums and Claims Warehouse) data source, using PySpark to compare data between the Actuarial Server, Oracle, and AIP, identifying mismatches across all sources and improving data accuracy by 40%.
Corrected premium transactions from ACE/CHUBB by mapping them to the right business lines in PySpark for Casualty, extracting and transforming over 15 years of historical data from Azure SQL, processing 10–20 million records, and eliminating manual data extraction.
Implemented multithreading techniques to parallelize data extraction from Azure SQL, reducing processing time from 8 hours to 3 hours, improving overall efficiency by approximately 60%.

Directed the biweekly migration and data integration of over 2 billion records from structured data sources to an on-prem Data Warehouse using SSIS, building data pipelines and a reliable repository for ANZ actuaries to ensure compliance and robust platform operations.
Coordinated with business users and stakeholders to gather detailed requirements while collaborating with DBA's to streamline post-ingestion processes and ensure smooth project implementation.
Orchestrated the transition of SAS scripts to R programming using RODBC. This migration substantially improved data management and accessibility, facilitating the transition of both technical and non-technical stakeholders from North America region, towards R for future utilization.
Engineered a Python automation script to generate personalized Price Monitoring Report templates across various business lines for North America region reducing manual tasks and saving 12 to 14 hours per report generation cycle. This resulted in a yearly time-saving of approximately 168 hours.
Utilized GitHub and JIRA for project documentation, improving project scope efficiency by 50%. Acted as a Subject-Matter Expert (SME) during requirements gathering for the Clarity project.

Implemented Python automation to streamline the preparation of RFPs (Request for Proposals), resulting in a significant reduction of 3 hours in manual work.
Successfully implemented a streamlined process change workflow and process improvements, resulting in a positive turnaround time and saving approximately 2 hours compared to previous methods.
Served as a key intermediary between Account Managers and Retailers, facilitating effective communication and gathering essential business requirements for the tender process.

Conducted thorough profile gathering and screening processes, utilizing referrals and several job boards, to ensure the submission of high-quality candidate profiles to the Client Manager.
Gathered and translated business requirements from hiring managers and clients into actionable talent acquisition strategies.
Provided support to Fortune 500 clients across 5 domains including Banking, Automotive, Retail, E-Commerce, and Healthcare, actively collecting market intelligence on relevant job positions.