Tim Johnson

Designed and implemented a data ingestion and transformation pipeline using Python, Airflow, and AWS Glue, automating the processing of unstructured media data into structured analytics-ready formats. Built a real-time data enrichment service in Node.js and FastAPI, integrating with third-party APIs and streaming events via GCP Pub/Sub. Developed and deployed a React + Next.js dashboard that visualized processed datasets and AI insights for internal product teams. Implemented ML models in PyTorch and TensorFlow for classification and clustering tasks, deployed via AWS SageMaker, enabling automated tagging of user-generated content. Led migration of core data workflows to a hybrid AWS/GCP environment, optimizing costs while ensuring scalability and reliability. Coordinated with product managers and data scientists to ensure end-to-end delivery of a production-grade data processing platform.

Architected and deployed AI-powered personalization engines for global retail clients using Python, TensorFlow, and AWS SageMaker, boosting recommendation accuracy and conversion rates. Designed Node.js + React micro-frontends to replace legacy monoliths, enabling faster feature delivery. Built scalable data engineering pipelines with Airflow and GCP BigQuery to process billions of user events monthly. Migrated services from on-prem to AWS Kubernetes (EKS) with Helm and Terraform, reducing infra costs and standardizing deployments. Created a real-time analytics dashboard with React, GraphQL, and D3.js to monitor live KPIs. Implemented event-driven microservices with Kafka and Node.js for near real-time order tracking and fraud detection. Introduced CI/CD using GitHub Actions, Docker, and AWS Lambda, reducing deployment times. Led a monorepo strategy with Nx and shared TS libraries, and designed ML-driven A/B testing framework. Optimized Elasticsearch clusters for performance and reliab