RAKHESH KASAGANI

Client: CDC (Centers for Disease Control and Prevention – Federal project)

Project: NBS Modernization

• Analyzed HL7 RIM Model to map relationships within the CDC NBS environment.
• Participated in Sprint reviews, backlog grooming, and planning for effective task prioritization.
• Optimized stored procedures, improving query performance and reducing ETL processing time.
• Demonstrated POCs using Talend, Nifi, and DBT for data migration to AWS S3.
• Developed Nifi and Talend ETL jobs, replacing SQL stored procedures with efficient pipelines.
• Proficient in Talend and Apache Nifi, conducting demos and building workflows for diverse data sources.
• Environment: Talend, Apache Nifi, SQL Server, JIRA, Confluence, Mural, Agile

Project: NIOSH

• Documented JIRA tasks and organized them in Confluence for clear tracking.
• Developed AWS Glue jobs to move data from S3 to various databases.
• Explored on-prem to AWS Cloud migration options.
• Built time series model POC using AWS Sage Maker and Jupyter Notebook.
• Assigned user policies and managed service permissions in AWS.
• Environment: AWS – (Glue, Athena, S3, RDS, EC2, Secrets Manager, etc.,)

Project: Sales Enablement Bot

• Created Sales Enablement chatbots using Microsoft Copilot Studio and Power Automate.
• Experienced in building topics, flows, and publishing bots on Microsoft Teams.
• Integrated Power Automate with AWS Lambda, handling AWS Signature between AWS Bedrock(RAG Bot) and Azure.
• Explored the options between Anthropic model vs Open AI. Choosen Claude v3 model to build the Rag Bot
• Retrieved AWS Signature via API calls between Azure and Lambda function url endpoints.
• Created Azure functions to handle the security between Azure and AWS endpoints
• Worked on configuring App registration for other applications to interact with Teams.
• Gained expertise in Generative AI and prompt engineering.
• Environment: Microsoft Copilot Studio, Power Apps, Microsoft Azure, AWS – Bedrock, Lambda function

• Worked in an agile scrum environment with a biweekly sprint
• Actively participated in backlog and refinement meetings considering the workload for various integrations
• Involved in gathering business requirements and documenting various attributes to a project
• Independently lead a project communicating with Business users and external vendor
• Also identified various new and constructive techniques to retrieve the data from Cloud by looping the process multiple levels down
• Implemented error handling and notifications inside the talend integration tool
• Improved ETL job performance by implementing variables on various internal configurations as well as applying various techniques to speed up the overall process
• Deep dive into triggering, executing, retrieving, and parsing the payloads and reports using talend data integration tool
• Used various components in talend to read, write and parse the data coming from various cloud systems and integrating them into another applications (involving Cloud and snowflake environments)
• Tested, peer reviewed and deployed the code into production as and when needed by the team
• Dealt with complex integration jobs by supporting the production fixes
• Analyzed various talend jobs and documented important steps to enhance the overall process
• Worked on workday data integration and supported the enhancements recommended by the business
• Retrieved various reports and payloads from API applications, parsed them and loaded into their target systems
• Worked on Azure Data factory to build pipelines and data flow for data migrations
• Designed and build ETL and ELT pipelines using Azure data factory and Azure Databricks
• Designed and build pipelines to migrate data from on-prem databases, rest API to Azure Data Lake Storage and Azure Synapse Analytics
• Retrieved data from various containers and transformed the data into complex structured files based on the requirements provided
• Connected and worked on various VM’s using RDP and Bastion
• Analyzed various settings to learn and enhance the Azure platform
• Created small framework to integrate various data flows via talend as well as Azure Data factory
• Environment: Talend Cloud Data Management platform, Artiva, Azure, GCP – google big query, Salesforce, NetSuite, Okta, GitHub, JIRA, Monday, Confluence, Saas, Zuora, Workday, Concur, Slack, Lucid chart, Elastic Academy (Dochbo), Elastic Cloud, snowflake etc.

• As a Data Integration Specialist, I used various techniques to identify, analyze and transform the data for various stakeholders and business partners
• Worked on various Predictive analysis projects considering future data (sales, weather, shifts, warehouse, etc.)
• Built various Data Integration solutions to handle batch/streaming data on ETL platform for various applications and microservices
• Strong understanding on relational database concepts and data processing concepts
• Integrated API calls, transforming Json files to the required format through SQL as well as Talend components and made available for reporting
• Used Talend Data Integration to send/retrieve data to/from Salesforce (internal & external)
• Also executed queries, reports inside Salesforce to validate the data flow
• Converted DataStage ETL jobs to Talend ETL’s with proper validations and unit testing
• Created Tidal/Control-m jobs to schedule the ETL jobs to run on a specific schedule (considering all the predecessors and successors)
• Great experience with TAC (Talend Administration Center) to setup users, grant required permissions, remove locks on the ETL’s, monitor job runs, etc
• Created Talend ETL processes to load source (files - txt, csv, xml, , Camelot DB2 database, Axway, ftp, other databases) to target (Hadoop, Netezza, SQL Server, files, APIs, Couchbase, S3, etc.) with required transformation
• Used Kafka and N1QL scripts to load data from Datawarehouse to Couchbase
• Used Talend Data Integration to integrate data between Datawarehouse and various application like SAP, Salesforce, SmartRecruiters (ATS), Pulse, S3, Couchbase, etc
• Resolved production issues and corrected/enhanced the ETL jobs to have the data integrity over all the environments (source, staging and target)
• Good hands-on Aqua, SSMS, GIT/STASH, JIRA etc
• Responsible in delivering assigned projects on a timely manner with desired quality
• Environment: Talend Data Integration 5x/6x/7x, DataStage 8x, Micro-strategy, DB2, Netezza, SQL SERVER, Salesforce, Aqua Data Studio, S3, Couchbase, Greenplum, Profisee, Kafka, ATS, API’s, Hadoop, Microsoft Azure, etc.
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o Domain: Health Care (State project)

o Project: Informatics for integrating Biology & the Bedside (I2B2) Enterprise Implementation

o Environment: I2b2 1.7.0, DataStage 8.7, SQL Server 2012, Oracle 11/12, UNIX, EPIC

o Domain: State (Tax - State project)

o Project: STARS Project

o Environment:ETPM, DataStage 8.5, Cognos, Oracle, PeopleSoft, UNIX, ESB, Agile methodology, Scrum meetings.

o Domain: Health Care

o Project: MTS FIN Optimization – BPR Data Conversion project

o Environment: IBM INFORMATION SERVER, ORACLE, HP 3000, PEOPLESOFT, UNIX, SAP, EPIC

o Domain: Health Care

o Project:Inventory Health

o Environment: IBM INFORMATION SERVER (8.1), ORACLE (9i), AUTOSYS, IBM AUX (5.3), VSS

o Domain: Health Care

o Project:Global Inventory

o Environment:DATASTAGE 7.5.2, MICROSTRATEGY 8, ERWIN, PROGRESS DB, ORACLE, PEOPLESOFT, UNIX, SHELL SCRIPTS, CRT-M

DataStage responsibilities:

• Engineered and executed IBM DataStage ETL processes, including design, development, and deployment
• Performed data mappings and transformations to move data from source to target systems
• Optimized ETL processes by identifying and resolving performance bottlenecks
• Collaborated in code reviews and deployed robust ETL scripts into production
• Utilized Parallel Extender stages (e.g., Remove-Duplicates, Sort, Funnel) to streamline data processing
• Created DataStage jobs using Stage Variables, Derivations, and Constraints
• Addressed discrepancies from User Acceptance Testing to ensure ETL process integrity
• Managed job transitions across DataStage versions with impact analyses
• Transformed UNIX shell scripts and PL/SQL into DataStage ETL jobs with documentation
• Supported DataStage production environment for stability and performance