Harshasri Mullapudi

Project: Dorman

• Worked on enhancing and maintaining existing AWS Glue-based ETL pipelines that process weekly file ingestions from various business sources.
• Optimized data transformation logic in Glue scripts using PySpark and Python to improve job efficiency and reduce runtime.
• Managed S3-based data lake architecture, including partitioned datasets and lifecycle policies for cost optimization.
• Integrated Glue jobs with EventBridge, Lambda, and Step Functions to automate file ingestion and error-handling workflows.
• Collaborated with cross-functional teams to implement incremental updates and schema validations for newly onboarded files.
• Monitored job performance via CloudWatch metrics and automated failure notifications using SNS.
• Contributed to continuous improvement by implementing enhancements in the existing data pipeline for better scalability and data quality.

GenAI Accelerator (Internal R&D Project):

• Contributed to a Proof of Concept (POC) to integrate Generative AI capabilities into data engineering workflows.
• Designed an LLM-powered metadata extraction pipeline using Amazon Bedrock and AWS Lambda to automatically generate dataset documentation.
• Developed a chat-based interface enabling team members to query Glue catalog metadata using natural language.
• Collaborated with the AI team to build prompt templates and evaluate model responses for accuracy and relevance.
• The project showcased measurable productivity improvements and formed the foundation for future AI-assisted data governance initiatives.

• As a Backend developer in ATIVE project developed new features and enhancements for the application using Python and AWS services like Lambda, DynamoDB, S3, CloudWatch, Event Bridge, Cognito, IAM, Athena, SQS, Step function, Glue job, API Gateway, Secrets Manager, ECR, EC2, Code Commit, Code Pipeline and Code Artifact. We will expose the result by using AWS Lambda microservices as RESTful API endpoints.
• Followed documentation and frequently connected with architects to understand the requirements to deliver the expected results based on the deadline.
• Worked on Data cleanup as a part of transformation on the Dataset using Pandas.
• Understanding the UX screen and came up with payload. For the desired payload wrote Json Schema and for each field implemented field level validation and tested from postman. Wrote detailed documentation in the confluence related to payload as a reference to QA and UI.
• Created multiple RESTful API endpoints with methods such as POST, GET, PUT, DELETE, OPTIONS, ANY and Integrated with Lambda.
• Occasionally updated the Yaml file for creation of new lambda.
• Solved 200+ bugs and any other issues by troubleshooting.

• An active team member to a project to transition the data warehouse workloads from Informatica to a Python/SQL framework, targeting Teradata Vantage.
• Conducted rigorous data validation during Unit testing to ensure the integrity of the loaded information.
• Implemented a comprehensive migration strategy, transforming existing ETL processes into Turbo Data Mover jobs employing BTEQ, PowerShell scripts, and Mload. These jobs efficiently extracted data from SQL Server and seamlessly loaded it into Teradata Vantage.
• Productionized the jobs with Zero Fallouts.