Steven Coker

Architected and maintained a large-scale AI/ML-powered data platform using Node.js, Python (FastAPI), React, and TypeScript, integrated with AWS (Lambda, ECS, S3, RDS) and GCP (BigQuery, Pub/Sub) for real-time insights and analytics dashboards. Built ETL and streaming pipelines with Apache Airflow, Kafka, and Snowflake; implemented real-time analytics components with WebSocket and GraphQL subscriptions; embedded AI insights (predictions, sentiment analysis, anomaly detection) into dashboards; designed modular frontend with Redux Toolkit; led CI/CD automation with GitHub Actions, Terraform, and Kubernetes.

Engineered and scaled data-driven web applications using Next.js, React, Node.js (NestJS), and Python (FastAPI), deployed across AWS (Lambda, S3, ECS) and GCP (Cloud Run, BigQuery) for analytics and automation platforms. Built interactive analytics dashboards with React + D3.js; developed real-time data processing systems with Kafka, Redis, and Airflow; integrated AI/LLM features using OpenAI API, LangChain, and Hugging Face Transformers; created GraphQL APIs with Apollo Server; designed and maintained large-scale ETL pipelines with PySpark and Airflow; standardized CI/CD with Terraform, Docker, and GitHub Actions.

Developed and optimized data ingestion and analytics pipelines using Python, Airflow, and BigQuery, processing terabytes of telemetry and operational data daily. Built microservices in Python and Node.js, connected data pipelines with frontend analytics tools and ML model APIs; implemented data validation and transformation workflows in PySpark and SQL; operationalized model training pipelines on GCP Vertex AI; created GraphQL gateways to unify data access and reduce API complexity; optimized BigQuery schema design and query performance to reduce costs; collaborated in Agile sprints to deliver high-performance analytics products.