I developed a comprehensive back-end server utilizing Python and Flask, which was deployed on AWS Lambda and API Gateway. This server seamlessly integrated numerous APIs into a front-end website, Android application, and various app scripts. Additionally, I implemented JWT authentication and role-based access control to ensure secure access across all the APIs.

I was a full-stack developer who contributed to the development of an Ion-based rule-execution platform that efficiently facilitated micro-service creation on the Amazon platform. During my tenure, I meticulously designed and implemented the following essential components: - An Ion data storage and retrieval solution utilizing DynamoDB and Elasticsearch. - A user interface portal powered by REST APIs, enabling seamless CRUD operations on all system resources for clients. - An on-box caching solution. - A robust permission control system to ensure secure data sharing among clients.

I have developed a native Android application for non-instant messaging purposes. In my role as a full-stack developer, I focused on designing the frontend utilizing material design components, while employing Back4App hosted Parse server as the backend solution.

Utilized AWS native technologies including Lambda, CloudAuth, and API Gateway for the implementation of a client-facing REST API, facilitating the direct claiming of Amazon e-Gift cards into customer accounts for external-to-Amazon clients. Established the AWS cloud formation stack to seamlessly connect API Gateway with Amazon's internal network of microservices. Developed a Java client library that was widely employed by various external clients for seamless integration with the API. Successfully mitigated the Lambda cold start issue by leveraging AWS CloudWatch events, resulting in a significant reduction of average latency by nearly 50%.

As a proficient back-end developer, I collaborated closely with seasoned senior developers involved in designing the HDFS export feature. Together, we comprehensively grasped the system's functionality to ensure the seamless implementation of a client-requested data pruning functionality for Sybase IQ-based virtual warehouses. This inclusive feature, in particular, validates the presence of data within the underlying HDFS prior to executing the pruning workflow, all while maintaining compatibility with previous versions and long-term maintainability.

I developed and executed an automated trading application in Python utilizing Interactive Broker APIs for the purpose of purchasing and selling securities. A sophisticated approach was employed, incorporating various technical indicators including DMI and ADX to facilitate informed trading decisions.

I successfully redesigned a data warehousing pipeline, resulting in a 70% reduction in operational burden for the team. This endeavor involved engaging with various stakeholders to discern between desirable and essential requirements for the streaming data. For instance, while some missing data was acceptable, duplicate data was deemed unacceptable. I conducted an in-depth exploration of multiple potential solutions, carefully considering the technical trade-offs associated with each. Eventually, I devised and implemented a new serverless solution using Fast Data Pipelines. This encompassed not only the design and implementation of a new service but also the incorporation of a backward compatible modification in all six related microservices. Additionally, I executed daily jobs involving SQL queries to reconcile data between the old and new solutions. Once the accuracy of the new solution was attested, I seamlessly transitioned to its utilization.