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QA Engineer with 4+ years in Azure & AWS Cloud Testing
I have 4.4 years of experience in QA testing, with a focus on Azure cloud and aws cloud testing. Proficient in creating and executing test scripts with the QBOT tool for accurate data comparison between source and target systems. Proficient in AWS testing, particularly data migration and validation using AWS services such as S3, Athena, and RDS. Working with Azure services like Azure Databricks, Azure Data Factory, Pipeline, Triggers, Azure Data Lake Storage, and Azure Blob Storage. Working with multiple databases such as Oracle, PostgreSQL, Databricks, and MS SQL Server. Well-versed with Bug Life Cycle. Expert in validating Slowly Changing Dimensions (SCD) Type-1, Type-2, and Type-3 tables, ensuring data accuracy and integrity throughout the ETL lifecycle Proficient in validating data integrity and quality using innovative solutions, with strong problem-solving and analytical skills. Having Knowledge of Test Management tools like HP QBOT and Test Rail. Having good experience in working with Linux.
LB PG College
MCA · Master of Computer Applications
August 1, 2019 – June 30, 2021
New Science Degree College
BSC · MPC
August 1, 2016 – June 30, 2019
Capgemini Technology Services India
ETL Tester
January 1, 2021 – Present
India
GENWORTH (Contact Centre Modernization)
January 1, 2021 – June 30, 2026
Genworth Financial Inc, established in 1871 as the Life Insurance Company of Virginia and headquartered in Richmond, Virginia, offers life insurance, long-term care insurance, mortgage insurance, and annuities. As part of our daily process, files from the NAS Folder are transferred to the Landing S3 bucket in AWS using AWS Data Sync. AWS Data Sync is a secure, online service that automates and accelerates the transfer of data between on-premises storage and AWS storage services. The data migration involves moving files from on-premises NAS Folder to AWS cloud storage services. We have multiple source systems, including CLOAS, CALYPSO, PEGA, PPLUS, and Amazon Connect. Currently, there are 594 files in the source, which have been converted into 594 tables in the Athena Database. Additionally, 17 tables from the EDH-T Population will be loaded into RDS. Athena views with the current snapshot are created by joining various source tables, as defined by the EDH-T domain table structure. Reduced manual efforts by 98%, ensuring smooth and efficient data processing.
P360 EDL to Cloud Migration
January 1, 2021 – June 30, 2026
This is an EDL to Cloud Migration project. Data comes from the SFTP path as a ZIP file daily. It is moving into stage folders and then into the Input path. Files will get consumed from the Input path after unzipping and the processed files will move into the archive path. We have tested multiple source feeds like E2open, BMC Helix, Gypsy, FCM, GCW Dependent Tables, and Masters. We have tested in parallel prod with the live data from the source which is EDL (Hive) and Target as FDL (Databricks). We have validated counts for each table and QBoT check for all the source feeds. We have uploaded test cases in Test Rail and attached the defects in Test Rail. Having good experience in preparing Test Strategy Documents and Test approach documents. We have tested it in ITG and parallel production environments and given it to Business validation. The report provided valuable insights into the data quality team, aiding them in identifying and resolving potential data discrepancies and enhancing overall data integrity. Reduced manual efforts by 95%, ensuring smooth and efficient data processing.
USAA Data Migration Project
January 1, 2021 – June 30, 2026
Migrated legacy system data to the Guidewire claim center (cloud-based solution). Highly configurable and customized to meet specific needs of the insurer.
Cultural Fit Analysis
The candidate's experience across diverse projects (EDL to Cloud Migration, Contact Centre Modernization, Data Migration for Guidewire) and clients (Hewlett Packard, Genworth Financial, USAA) indicates adaptability and exposure to different business domains and technical stacks. This diversity, coupled with their proficiency in both Azure and AWS cloud environments, suggests a strong capacity to integrate into varied technical cultures and contribute to different types of initiatives. Their role as an ETL Tester aligns well with a QA Engineer position, particularly one focused on data quality and migration.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills, as evidenced by their work in identifying and resolving data discrepancies, performing Root Cause Analysis, and ensuring data integrity. Their experience in preparing test strategy documents and conducting knowledge transfer sessions indicates good organizational and collaborative capabilities. The focus on reducing manual efforts suggests an efficiency-driven mindset, which is valuable for operational improvements.