Karishma Khanna

Responsibilities:

• Successfully grasped the company's needs for handling used branded product listings with an emphasis on image quality and authenticity verification.
• Designed a robust AWS cloud architecture tailored to process and validate high volumes of product images, integrating AWS Lambda, Redshift, S3, SQS, and SNS.
• Led the development and deployment of automated workflows using AWS Lambda for image retrieval and analysis, initiating approval processes based on risk assessment of brands.
• Coded solutions in Lambda, hosted on Docker for efficient scaling; integrated with SQS for queue management and SNS for real-time notifications.
• Utilized SQL for data management, ensuring efficient storage and retrieval of process data.
• Configured IAM roles for secure access management and set up EventBridge for automated task execution four times daily.
• Developed mechanisms for alerting via email in cases of process failures, enhancing system reliability and operational awareness.

Project Name: Data Ingest

Technology: AWS, Python, Pandas, Numpy, Lambda, Jenkins, Groovy

Description: Build an end-to-end solution for client data by processing their files near real time.

Project Contributions:

• Performed Data Quality checks on data with Pandas and Numpy and implemented SQL with Pandas to implement Client Business Logic
• Development of Data Quality & ETL Checks on Lambda functions.
• Deployment of Lambda functions, Lambda Layers using Git and Jenkins Pipeline.
• Responsible for building technical documents and topology diagrams for deliverables.
• Played role in enhancing internal processes, driving efficiency improvements, and fostering innovation.
• Analyzed processes to handle Infrastructure Failures for Lambda Functions.

Project Name: Global Reporting Solution

Technology: Talend Big Data, Amazon S3, Redshift, Python

Project Description : Developed and designed a multi-country model for client, offering different business development for each country. The project was built on Redshift and orchestration through Talend with managing the conversion of files through Python.

Project Contributions:

• Implemented Python to read the source files of different formats, perform quality checks on files & dumped the files to S3 landing zone by converting them to standardized format.
• Analyzed business requirements and source data for various client countries
• Created dynamic workflows to read files from S3 to redshift and dumb files to Archive folder after each read for each country.
• Proposed stored procedures for representing data at different time period.

Project Name: GTP

Technology: Python, Microsoft Azure (SQL Data Warehouse, Data Factory, ADLS-Gen2, Azure-Devops, Virtual Machines, Function App)

Project Description: Developed a secured data transfer environment on Azure using on-premise and Azure data sources with user-level and data-level permissions.

Responsibilities :

• Implemented Pandas and Numpy to clean client data.
• Performed Data Quality checks on data and implemented SQL with Pandas to implement Client Business Logics.
• Managed the configuration of the Azure Environment to establish secure connections between on-premise and off-premise resources, while also ensuring resource and user-level access for the client.
• Created Key Vault & Azure Data Pipelines, configured Virtual Machines, Function App for Python, Databricks environment and connected them securely with Azure Network.