Enterprise-Scale Data Architecture
Designed and implemented a complete medallion architecture (bronze/silver/gold layers) processing petabytes of chess game data.
Real-time streaming pipelines handle millions of concurrent games, while the transformation layer prepares data for AI/ML models
that power Chess.com's recommendation engine and player analysis features.
Real-Time Kafka Streaming Pipeline
Confluent Kafka infrastructure processing millions of events per second — live game moves, user interactions,
and system telemetry. Custom producers and consumers optimized for Chess.com's unique scale, with exactly-once
semantics and sub-100ms end-to-end latency for real-time features.
BigQuery Analytics Powering Recommendation Systems
Petabyte-scale BigQuery warehouse serving as the analytics backbone for Chess.com's AI systems. Optimized
partitioning and clustering strategies enable sub-second queries across billions of games. Powers recommendation
engines, player matching algorithms, and content personalization for 200M+ users.
dbt Transformation Layer Enabling AI/ML Use Cases
Comprehensive dbt transformation framework preparing data for machine learning models. 500+ models with
automated testing, documentation, and data contracts. Feature engineering pipelines feed recommendation systems,
player skill estimation models, and content personalization algorithms. Incremental processing ensures fresh
features for real-time ML inference.
Apache Airflow
Workflow orchestration with custom operators for data processing. Dynamic DAG generation, robust error handling, and integration with monitoring systems for reliable batch processing.
ML Feature Store & Model Serving
Production feature store serving real-time and batch features to Chess.com's ML models. Powers player
skill rating systems, content recommendation engines, cheat detection algorithms, and engagement prediction
models. Integrated with Vertex AI for model training and deployment.
Chess.com