Senthilkumar Perumal

• Project: Claims Triage Automation - Event-Driven Data Platform Modernization
• Overview:
• Leading the design and implementation of an event-driven, AI-enabled data platform that combines
• Key Contributions:
• - Designed AWS Lambda + SQS + Glue-based ingestion to normalize high-volume nested JSON into Snowflake.
• - Built recursive JSON shredding logic using Snowpark and surrogate keys for child-parent linking.
• - Integrated Snowflake Cortex to infer schema and classify tables/columns dynamically.
• - Implemented Snowflake Streams + Tasks for near real-time incremental CDC processing.
• - Created end-to-end Prefect v3 orchestration with alerting and rollback features.
• - Maintained observability dashboards and alert triggers with Snowflake Alerts.
• - Led upgrade and incident management responsibilities as TAO across Snowflake and Prefect ecosystems.
• - Provided training, playbooks, and mentoring to cross-functional teams.
• Technology Stack:
• AWS (Lambda, Glue, S3, EventBridge, SQS), Snowflake (Streams, Tasks, Cortex, Snowpark, Alerts), Prefect

Project: Claims IT Data & Reporting – Unified View for Claim Notes

Overview:

Designed and developed high-performance data pipelines to create a unified view of claim notes by integrating AWS live streams and legacy DB2 historical data in a cloud environment. This unified dataset supports analysts, BI teams, and data scientists for advanced modeling and querying.

Key Contributions:

• Built and managed AWS infrastructure using Terraform to automate resource provisioning for ingesting JSON datasets into S3.
• Developed AWS Glue jobs and PySpark scripts to extract, normalize, and partition high-volume JSON data for efficient processing.
• Configured Glue Crawlers and schedules to update table metadata and optimize S3 partitioning.
• Created Snowflake external tables and leveraged COPY commands to ingest S3 data into raw tables.
• Collaborated with business analysts to document data lineage and finalize requirements.
• Wrote complex SnowSQL queries to join, transform, and load data into ODS and target layers, producing unified claim note views.
• Implemented CDC mechanisms using Snowflake Streams to efficiently capture and process data deltas.
• Supported QA teams by troubleshooting bugs and addressing change requests.
• Developed Python workflows orchestrated with Prefect for pipeline automation, monitoring, and alerting.
• Established reconciliation processes with pre- and post-validation queries to ensure data consistency across source and target tables.

Technology Stack:
AWS (Glue, Kinesis Firehose, EventBridge, S3, Athena), Snowflake, PySpark, Terraform, GitHub, Jenkins, Prefect

Project: Cloud Data Migration & Reporting Enablement

Overview:

Responsible for architecting and building AWS infrastructure and services to support the migration, processing, and storage of data in AWS S3 and Snowflake. Led end-to-end data extraction from source systems, transformation, and loading into cloud-based targets, ensuring seamless data availability for reporting teams both in the cloud and on-premises.

Key Responsibilities:

• Designed and provisioned AWS infrastructure (Glue, Lambda, S3) to support scalable data processing and migration initiatives.
• Extracted, transformed, and loaded data from PWD/Hadoop and other source systems into AWS S3 and Snowflake.
• Set up and configured Snowflake environments and services to make data accessible for cloud and on-prem reporting teams.
• Collaborated with Data and Reporting teams to migrate critical datasets from legacy platforms to the cloud.
• Developed PySpark and Python scripts for data transformation, normalization, and workflow automation.
• Enabled secure access to cloud data from on-premises environments, ensuring business continuity and usability.
• Mentored junior developers, fostering skill growth and enhancing team collaboration.
• Conducted code reviews, ensuring adherence to best practices and high-quality standards.

Technology Stack:AWS (Glue, Lambda, S3), Snowflake, PySpark, Python

Project: CLDW – Commercial Lines Data Warehousing

Client: The Hartford Insurance
Overview:
Supported agile product delivery and automated ETL validation, data validation, and status reporting for commercial lines data warehousing. Identified cross-sell opportunities from acquisitions/mergers, maintained code repositories, and ensured seamless CI/CD operations.

Key Contributions:

• Designed and maintained data architecture, ETL design, and reporting capabilities.
• Set up CI/CD pipelines for PLSQL, Talend, AWS EMR, and Snowflake using GitHub, Jenkins, Udeploy, and Liquibase.
• Developed data ingestion and processing pipelines with Apache NiFi, Amazon EMR, Spark, PySpark, Athena, Glue, and Data Pipelines for Policycenter Guidewire data into S3 Data Lake.
• Guided developers on CI/CD best practices and pipeline usage.
• Collaborated with scrum and systems teams to refine release strategies.
• Created account and policy-level datasets, performed SQL queries for metrics, and implemented MDM for data cleansing.
• Built dashboards, reports, and automated ETL execution, validation, and reporting.
• Deployed custom tables, views, procedures, and indexes to SQL Server for staging and data-mart environments.
  Technology Stack:
  Linux, Talend, Informatica PowerCenter, Liquibase, Autosys, AWS EMR, Snowflake, AWS S3, GitHub, Jenkins, Nexus, Oracle, MSSQL Server

Project: AWS Adaptive Data Foundation

Client: Merck & Co. (Pharmaceutical)
Overview:
Enabled complete and accurate migration of datasets from zCloud mainframe to AWS, replicating reporting functionality in the cloud and optimizing data access and performance.
Key Contributions:

• Advised on technical decisions for data intelligence and cloud provisioning.
• Built a responsive cloud data ecosystem for sourcing, transforming, and consuming data.
• Designed optimized reports, queries, S3 folders, and file rationalization for Athena.
• Performed post-archival validation to ensure data integrity.
• Developed AWS IAM role-based access controls for S3 data.
• Participated in AWS foundational planning and growth.
• Migrated Informatica ILM TDM applications to AWS EC2 reserved instances.
  Technology Stack:
  Talend, DMX-SyncSort, AWS (S3, EC2, Glue, Athena, CLI), IBMDB Mainframe, Virtual Tapes

Project: Data Governance and Security

Client: The Hartford Insurance
Overview:
Designed and implemented data masking and subsetting strategies to ensure production data is not used in non-production environments, enhancing data privacy and compliance.
Key Contributions:

• Led data masking strategy and ILM framework design for application decommissioning.
• Estimated effort and forecasted resources for future projects.
• Coordinated with stakeholders for smooth project execution.
• Developed masking and subsetting methods in Informatica PowerCenter and TDM.
• Loaded lower environments with PII-protected production data.
• Rapidly profiled data for analysis and validation.
• Built BI reports to analyze vulnerabilities in PII and sensitive data.
• Designed ETL pipelines for future business process integration.
  Technology Stack:
  UNIX/Linux, ILM Test Data Manager, Informatica TDM/PowerCenter, ILM Data Validation Option, DDS, MongoDB, Oracle, MSSQL, AWS CloudWatch, CloudTrail, IAM, Jsonar

Project: Information Lifecycle Management (ILM) – Application Retirement & Live Archival

Client: Genentech, Inc. (Biotech)
Overview:
Decommissioned legacy applications (SAP, Siebel, Oracle/MSSQL) using Informatica ILM, maintaining archived data for compliance and reducing maintenance costs.
Key Contributions:

• Collaborated with customers to understand system requirements and retirement solutions.
• Coordinated with Informatica support and R&D to resolve critical issues.
• Provided feasibility analysis, proof of concepts, and business presentations.
• Analyzed requirements and resolved postproduction issues related to retirement.
  Technology Stack:
  Informatica ILM Data Archive, ILM Data Validation Option, JReports

Project: Medsupp Migration

Client: Anthem, Inc. (Insurance)
Overview:
Migrated Medicare Supplement business from legacy mainframe to Medisys platform, designing ETL routines and ensuring secure, accurate data transition.
Key Contributions:

• Developed and maintained ETL maps/scripts and data models.
• Built and tested Informatica mappings, sessions, workflows, and tasks.
• Executed data extracts for legacy application retirement.
• Defined transformation rules and mapped IMS segments to relational tables.
• De-identified PII and sensitive data in test environments.
• Performed code reviews and unit testing, supporting QA and release management.
  Technology Stack:
  Mainframe Z/Os, Linux, Informatica PowerCenter, PowerExchange Navigator, IBM Optim, COBOL, DB2, Oracle

Project: ARC Program – Architecture Rationalization and Consolidation

Client: Barclays (Banking & Investment)
Overview:
Rationalized and consolidated architecture to simplify systems, reduce legacy dependencies, and enable strategic replacements.
Key Contributions:

• Implemented data archival solutions and developed business objects.
• Built automation frameworks for archiving and reporting.
• Designed and developed secured SSRS reports on archived and live data.
• Analyzed and optimized SQL performance for source/target databases.
• Interacted with SMEs and vendors for requirements gathering and reporting.
  Technology Stack:
  MSSQL, Intersystem Cache, Informatica ILM, SSIS, SSRS