♦ Spearheaded the data persistence layers using multi-table design within DynamoDB, considering single-table design and opting for better separation of concerns, with a focus on low latency and high elasticity in using DynamoDB

♦ Leveraged Typescript, Node.js, and AWS SDK v3 to create the Accounts Management Service, effectively maintaining sub 250ms p50 latency across accounts queries, improving the user experience and maintaining high scalability

♦ Developed the API Gateway, Lambda, and DynamoDB Email Signup Service utilizing AWS CDK v2, crafting the Lambda function in Python with the boto3 library to persist data in DynamoDB for clients registering for notifications

♦ Pioneered the ML Sentiment Analysis Integration, utilizing AWS Bedrock running serverless, stateless, and secure Foundation Models (Anthropic and Amazon Nova) for executive summaries and analysis of encrypted messaging

♦ Improved internal-user experience by developing on enterprise-scale docket entry quality control (QC) overhaul, consolidating work item displays across case groups and eliminating redundant manual processing for docket clerks

♦ Implemented RBAC middleware for Zendesk-integrated user deactivation workflow, ensuring secure administrative operations through granular permission validation

♦ Cut CI/CD pipeline time by ~55% by reducing GitHub Actions checks from 32 to 15 minutes (Node.js dependency caching + test parallelization) and CircleCI deployment from 42 to 19 minutes (p-limit concurrency, batch processing with Promise.allSettled(), and removal of obsolete jobs)

♦ Architected the Virtual Machine Observability Dashboard leveraging Python scripts for automated data aggregation and analysis, increasing operational insight

♦ Optimized data retrieval by developing Express middleware to efficiently process and shape incoming data, streamlining request handling, reducing server load, and enhancing response times

♦ Enhanced user experience using Chart.js and TypeScript to visualize AWS EC2 data, enabling users to monitor resource usage while reducing runtime, improving data accuracy, interface reliability, and enforcing type integrity

♦ Spearheaded the development of the EC2 Integration System using Node.js and the AWS SDK to ingest VM health metrics from CloudWatch logs and alerts, ensuring smooth server-client communication for real-time data updates

♦ Developed robust CI/CD pipelines for automated deployments using GitHub Actions and Python-based scripts, reducing deployment failures

♦ Migrated from Amazon SDK v2 to v3, optimizing performance and security, reducing import footprint from 10.7 MB to 733 KB, including 245.1 KB for AWS EC2 and 237.8 KB for CloudWatch, decreasing memory usage and improving load times

♦ Conducted 28% of unit testing coverage of critical application components using Jest’s inherent mocking and scalable testing capabilities, ensuring high code quality and boosting confidence among internal stakeholders

♦ Established JavaScript and Apex solutions for a comprehensive data cleansing process during the migration to Salesforce from Raiser’s Edge by using native duplicate management tools to identify, merge, and remove duplicate records

♦ Prevented further data inconsistency by automating customer and donor input management in Salesforce through template utilization, enhancing data accuracy for 75 donors daily

♦ Implemented IME Software Cloud Platform, ensuring data consistency across clients, patients, attorneys, and examiners, improving operational efficiency by 25% and reducing errors by 40%

♦ Developed and deployed custom websites and web apps on Netlify using HTML, CSS, JavaScript, and React, enhancing client engagement and driving 20% business growth