- AWS DynamoDB: Leading a team of 11 Software Engineers in AWS DynamoDB that powers customer features such as Exports to S3 and Point in time Recovery (PITR)- AWS FinTech team: Effectively managed a team of 12 engineers (10 software Engineers and 2 Data Engineers) with regular one-on-ones, setting learning and development goals, identifying growth areas and providing feedback. Managed career growth, promotion and low performance in the team- AWS FinTech team: Led the team through software development lifecycle for launching a new Revenue Planning and Forecasting product in 4 months (June – Oct 2021). This product handles multi-billion $ Revenue Planning for AWS Services - AWS FinTech team: Increased adoption of the Revenue planning and forecasting product by launching 10+ features achieving 100% adoption in 8 months from product launch. Awarded team excellence award for finance in AWS All-Hands in June 2022 for this achievement- AWS FinTech team: Led the operational review program across 23 team in AWS FinTech org. Developed mechanisms to evaluate operational health of systems and drive completion of operational issues- Conducted 60+ interviews for Amazon. Successfully hired 3 Software Engineers on the team in the span of 4 months- Devices Advertisements team: Worked with Senior Engineers from three different orgs to re-architect rotating banners on Amazon’s Homepage. This effort enabled all orgs in Amazon such as Devices, Prime, Fashion to personalize ads on rotating banners on Amazon’s Homepage in May 2021- Payments team: Led the team in identifying requirements, design and development to achieve the business goal of automating multi-million $ accounting entries for post settlement payment transactions in Europe in May 2020- Payments team: Led the team to develop a new internal portal to manage failed direct debit customer payments, chargebacks, re-payments, disbursements in 2019. This portal manages multi-million $ transactions per year

- Led the design and development of functionality to route customer’s voice shopping request to the appropriate Amazon Store (Amazon, Whole Foods, Prime Now) by evaluating product availability and customer propensity. Also developed an interactive voice feature to give customers the ability to choose the desired shopping store- Designed and developed a new interactive voice guidance feature on Alexa to help customers narrow down the search to specific products by asking questions for vague search requests. This increased the shopping conversion rate by 100% for vague search requests- Integrated Query Understanding Service with the Product Search system to understand different attributes in Search Query like brand, size etc. Designed and Developed parts of Product Search Relevance model to improve the relevance of the Search Results. Developed a test bed to evaluate the precision and recall of the new product search relevance model. Used the test bed framework to simulate customer traffic and tune the relevance model to improve search results

- Designed and developed automated workflows to vertically scale Redis clusters and perform version upgrade on Redis clusters. This enabled the customers to scale up memory and upgrade Redis version while running Redis servers without any data loss and down time- Architected an asynchronous health check mechanism for Redis nodes which improved the customer experience by eliminating the false positives in failure detection- Developed database reconciliation solutions to eliminate any inconsistencies across multiple databases and servers.This reduced the customer impact time from 1 hr to 5 mins- Implemented memcached persistent connection pooling using Apache Commons Pool to communicate with Memcached engines efficiently- Handled multiple large scale failure events and customer issues

-Worked for Prof Juliana Freire in the field of Open Source Data Analytics and Visualization.-Designed and Developed an "Automatic Data Type Detection and Classification Engine" which uses multiple Similarity metrics and Hierarchical Agglomerative Clustering to find same attributes types in the data.

-Worked as an Intern in Buyer Side Fraud team in Transactions Risk Management Department at Amazon-Implemented a Rule Management Engine which allows Fraud Investigators to dynamically add new Rues and modify existing Rules while investigating Fraudulent orders-Designed and Developed a Day Time Call Queue which detects customer's time zone and helps Fraud Investigators to call the customers during customer's Day time

-Developed Data cleaning algorithms for Open source Datasets