Devipriya Rajaguru

12.6 years of experience in the IT Industry. 8 Years of experience in ETL (Informatica Power Center), Oracle PL/SQL and UNIX. 4 years of experience in Amazon Web Services (AWS) and Python. 6 months of experience in Onshore (USA – Colombia, Ohio).

• Implemented Cloud Solutions using various AWS Services including EC2, EMR, S3, RDS, DynamoDB, Route53, EBS, EFS, ELB, AG and AWS Lambda functions. AWS IAM and security groups have been configured in VPC's public and private subnets. Created AWS Glue jobs to load and extract data from different sources to Amazon RDS, Athena and Amazon SNS to send automated alerts.
• Built Python scripts using Pandas and Boto3 for data validation, cleansing, and transformation.
• Good experience in ETL - Informatica Power center. (Mapping designer, Mapplet designer, Workflow manager, Workflow monitors various transformations such as Source qualifier, Lookup, Aggregator, Router, Expression, Joiner, Union & Sequence Generator) and run the informatica workflows using unix (pm cmd) command.
• Good Exposure in UNIX commands (awk, find, grep, sed, cp, mv, rm, sftp, scp, tar) and shell scripting.
• Have good exposure to write basic SQL queries to complex queries. Ability to write Stored procedures, Functions, View, Indexes, Triggers, Cursor, Sub query, Packages.
• Have resolved P2, P3 incidents during production support activities.
• Have Communication skill to interact with co-workers and users to high level of service provided to company / customer also have a working experience in Onsite and Offshore work model.

Model Factory (MF1 & MF2) | AWS, AWS Glue, Python, MySQL | Accenture Technology Solutions,Team Lead | Jan 2022 to Till

Cloud-based insurance data processing and notification platform to ingest policy, claims, and premium transaction data from various channels, perform data validation and transformation using Python, store processed information in AWS services, and trigger automated alerts for critical business events like claim status updates, policy lapses, and renewal reminders.

• Interacted with clients and developed and maintained a cloud-based insurance data processing platform using AWS and Python.
• Configured Amazon S3 buckets for storing raw input files, processed output files, and archival insurance records.
• Developed AWS Lambda functions using Python to automatically trigger data processing workflows whenever new files were uploaded to S3.
• Built Python scripts using Pandas and Boto3 for data validation, cleansing, and transformation of policyholder, claims, and premium payment records.
• Created transformation logic to convert raw transaction data into standardized business format for downstream reporting and claims processing systems using Script or AWS Glue.
• Integrated processed data storage with Amazon RDS for secure storage of policy and claims information.
• Developed automated claim status update workflows to notify business teams and customers whenever claim records were approved, rejected, or pending review.
• Configured Amazon SNS for sending automated alerts and notifications
• Used AWS CloudWatch for monitoring Lambda executions, error logs, file processing status, and application health.
• Implemented IAM roles and policies to ensure secure access control for AWS resources and sensitive insurance data.
• Optimized Python data processing scripts to improve performance and reduce processing time for high-volume insurance transaction files.

Distribution Partner Key Phase 2 (DPK) | Informatica, Oracle PL/SQL, Unix | Accenture Technology Solutions, Application Development Senior Analyst | Oct 2019 to Dec 2021

Developed alternate data views in the Distribution Partner Key Phase 2 project to provide historical customer and agent channel details in the required format, reducing dependency on fragile legacy views impacted by policy system migrations.

• Prepared requirement understanding documents, Technical Design Documents (TDD), and functional documents based on business requirements and project specifications.
• Designed and developed 3-stage ETL data flow architecture involving mappings, sessions, and workflows for populating data into target Oracle tables.
• Implemented data transformation and business logic in Stage 2 mappings, including validation, data cleansing, and required field-level transformations.
• Configured workflow parameters and default values by maintaining them in Oracle tables to dynamically generate parameter files and pass runtime values to workflows.
• Created and scheduled respective jobs in ESP for automated workflow execution and batch processing.
• Performed unit testing, system testing, and regression testing to ensure data accuracy and workflow stability, and prepared all required documents for business sign-off.
• Supported production migration activities by preparing deployment artifacts, migration scripts, and release-related documentation.
• Monitored pre-run and post-run production jobs after migration, validated workflow execution status, and coordinated with business teams for production data verification.

System of Authority (SofA) | Informatica, SQL, Unix | Cognizant Technology Solutions, Associate | Nov 2015 to Oct 2019

A System of Authority (SofA) is a data service provider within the CDW (Corporate Data Warehose). It provides data integration and distribution services for Systems of Record. Its primary data service is being a central data repository service provider of historical and current data element point in time states for Systems of Record or a System of Authority. New feed from CDW Oracle to GCSS and ONEDATA SQL , CDW Oracle to ONLINEHIP MySQL and Salesforce to SOFA.

• Added Full and Incremental logic for interface specific and User defined common UDF to achieve business requirements.
• Data transferred into required format and created different target files for respective source systems.
• Target files are delivered to the respective FTP locations.

MDM (Master Data Management) | Informatica,SQL ,Unix | Tata Consultancy Services Ltd, System Engineer | Mar 2013 to Nov 2015

Master Data Management (MDM) Systems takes sources from ESB[XML] and provides the input to different Target Systems. In this system, Source Message data is available in the queues(JMS) for specific interfaces. Source data will be different messaging queues which will be further transformed to target systems in the way data are expected to deliver. Target's OASIS, SPRIT, GPS, OXFORD, SOCRATES and PEUOPS.

• JMS Connection Creation based on interfaces and each used its own JMS messages, using Oracle CLOB data type we stored for all the interface xml’s.
• Views are created to extract the data from xml.
• Target files are delivered to the respective FTP locations.

I hereby declare that all the above given details are true to the best of my knowledge and belief. 

Place: Chennai      Date:             

Yours sincerely,

Devipriya R