Daniel Jin

About

Backend/data engineering candidate specializing in Python/Flask services and ML-enabled systems, deploying containerized workloads on AWS ECS/EKS with strong observability (Prometheus/Grafana) and PostgreSQL performance tuning. Built multi-tenant architectures with row- and schema-level isolation and optimized a Kubernetes-based Airflow + Spark nightly ETL pipeline for an e-commerce client, improving performance by 250%+ and reliably beating morning reporting deadlines; also contributed to Apache Airflow (SQLAlchemy/PostgreSQL area).

Experience

Education

Key Strengths

• Designed modular Flask backend architecture with clear separation of concerns
• Production deployment experience with Docker on AWS ECS/EKS using Gunicorn + Nginx
• Database performance optimization with SQLAlchemy/PostgreSQL (indexes, composite indexes, eager/lazy loading, N+1 avoidance)
• Strong query profiling/monitoring practice (EXPLAIN ANALYZE, slow query logs, pg_stat_statements)
• End-to-end ML workflow integration into backend services (model loading, preprocessing, inference)
• Built and optimized large-scale ETL/ML pipelines with Airflow + Spark on Kubernetes/EMR
• Improved nightly pipeline throughput by >250% via Spark tuning and Kubernetes/Airflow orchestration changes
• Multi-tenant data isolation design across app, DB, and infrastructure layers (row-level, schema-level, S3 prefixes, K8s namespaces/IAM)