● Spearheaded the successful launch of a feature supporting the Amazon Bulk Services program, potentially saving Amazon Business an estimated $161M annuallythrough streamlined processes and increased efficiency● Designed and deployed a feature aimed at solving the challenge of managing payloads exceeding 256KB in AWS SQS, facilitating seamless ingestion of data fromnew streams across services● Engineered monitors and alarms with AWS CloudWatch, leveraging Python to analyze live data streams with 40,000 TPS from diverse sources, facilitating businessmetric generation and automated graph creation for Weekly Business Reviews, ensuring effective service health observation and future project identification● Implemented automated integration tests within a comprehensive CI/CD pipeline, enabling rapid bug detection within a 90-minute timeframe post-productiondeployment ensuring high code quality through automated rollback mechanisms in case of failure● Managed the creation, updates, and maintenance of Material Definitions in a robust DynamoDB database containing ~3M products, serving as the primary source forfulfillment across global internal teams, guaranteeing timely access within a 24-hour SLA● Resolved over 100 tickets with immediate customer impact, while also providing continuous support to team services during monthly 24/7 on-call rotations● Operated within cross-functional teams in an agile framework, collaborating to construct product backlogs and engaging in backlog refinement sessions to discernactionable tasks for forthcoming Sprints along with conducting code reviews and providing feedback to maintain high code quality

• Mentoring 22 freshmen at university to locate resources around campus and assist with any of their queries • Guiding learners using MentorHub NU application which keeps a track of student habits to help achieve their goals

• Recommended promotions for ~2,400 items which helped the client generate 2% more revenue than the previous year• Implemented cannibalization technique using R to figure out cannibalized products• Cleaned and performed Exploratory Data Analysis using Python libraries NumPy and Pandas• Derived insights using R and SQL to understand customer data to forecast sales value better• Created an allocation tool that helped the client to allocate multiple products at a time and saved ~5 hours of manual work

• Discovered top 10 key categories and key items that uplifts revenue for a client using machine learning algorithms • Documented client requirements in client meetings for team reference• Found similarity between products using cosine and Levenshtein distance method to generate similar products using R