Architected and led a team of engineers to deliver a new enterprise-ready UI using React and GraphQL, coordinating feature breakdowns, technical design, and delivery timelines.

Designed and delivered large-scale initiatives including a Document Management integration, a feature-gating system for systems of record (SORs), and enterprise UX improvements for high-volume clients.

Interviewed and mentored engineers onboarding to the team, while serving as a subject matter expert across frontend, backend, and integration services, and establishing shared engineering frameworks and best practices.

Leveraged AI development tools (Claude, AWS Q, GitHub Copilot) to accelerate design exploration, code reviews, and common codebase workflows, improving personal productivity and team-wide development efficiency.

Updated and implemented backend services in Node.js with PostgreSQL, developing APIs and data models to power core HR automation logic, data, and integrations.

Upgraded rendering pipeline CI/CD from local to Github Actions. Utilizing Serverless to deploy a Cloudformation stack with five AWS Lambdas, one EC2 worker, with linked S3 for rendered artifacts

Designed and implemented upgrade to rendering pipeline for two resources with different dimensions, adding company logo on USPS approved area based on feature flag per client

Restructured event driven integration for HR hiring software to Azure cloud hosted Flask web server.

Implemented internal web application using Flask and Vue.js, implemented url routing, object models, server configuration, and api design.

Configured continuous integration and deployment for all owned services.

Processed data between the Facebook Business API and client Django web application using Python, MongoDB, and PostgreSQL.

Performed system administration and configured the technology environment for clients with over 100 employees.

Programmed Ruby on Rails based microservices that implemented MySQL databases for all data sets, triggers, history, feeds, and workflow processing metadata.

Enhanced an ETL Java pipeline that performs big data transformations, serialization, and reporting on custom data sets and loads them to cloud databases.

Implemented a Ruby microservice that communicates with datasinks in HDFS clusters and AWS S3 buckets to preview client uploaded files for data set transformations.

Deprecated a Ruby service that read datasink changelog entries and replaced functionality with a recurring Java workflow, updating database metadata in batches for HDFS. For files uploaded to S3 buckets, replaced with an AWS Lambda for higher efficiency.

Reduced data set processing time by 30% by implementing a Ruby delayed job wrapper and kick off.

Increased the success rate of data set error table population from 40% to 100% success rate by reconfiguring dependency injection with the Guice API while initializing the processing pipeline.

Managed configuration and automated deployment of jobs and services across environments using DC/OS, Spinnaker, Chef, Spork, and Docker.

Investigated and resolved support requests from clients and consultants using Jira.