Projects
Three end-to-end projects showcasing streaming, lakehouse, and AI-assisted data engineering. Each repo runs locally with synthetic data, ships CI, and publishes measured results. The previews below are real artifacts from each project's demo path. Click any card for a live, interactive walkthrough of the architecture.
Project 01
Kafka -> Spark Structured Streaming -> Delta -> dbt -> Streamlit. Exactly-once processing, anomaly detection, dbt tests.
Streaming ingestion + watermarks · Data quality / dbt tests · Fraud analytics architecture
~110k/s detector throughput
Project 02
Medallion (Bronze/Silver/Gold) lakehouse over synthetic CDR data. Airflow + MinIO + Iceberg + Great Expectations + dbt.
Medallion lakehouse design · Data contracts at the boundary · dbt modeling for BI
0.53s raw to Gold, 49,998 rows
Project 03
CLI that uses Claude to suggest Spark/Snowflake query rewrites and partition strategies. Benchmarked against a seeded corpus with explicit ground truth.
Applied LLM tooling · SQL parsing + heuristics · Benchmark methodology
0.67 keyword overlap vs ground truth