I build production-scale data infrastructure that powers business decisions at enterprise scale.
- 🏗️ Architected a data lake serving 2,200+ users and $1.8B+ in marketing budgets
- ⚡ 99.9% pipeline availability across 8+ enterprise business units
- 🎯 33+ technical interviews conducted as Amazon interview panelist
- 🛡️ AWS Certified Data Engineer – Associate
- 📍 Based in Seattle, WA
Data Platforms: Apache Iceberg · AWS Glue · Amazon S3 · Data Lakehouse Architecture
Streaming & Orchestration: Amazon Kinesis · Apache Airflow (MWAA) · Dynamic DAG Generation
Warehousing: Amazon Redshift · Amazon Athena · DynamoDB · Redshift Spectrum
Governance: RLS · CLS · PII Masking · Data Contracts · DLQ · Metadata Management
| Project | Description | Tech |
|---|---|---|
| data-platform-quicksight | End-to-end data platform — Kinesis → Glue → Iceberg → Redshift → QuickSight with automated self-serve analytics | Python · AWS · Airflow |
| StockGPT | AI research agent for stock market analysis — Gemini 2.5 + FastAPI + React/TypeScript + DuckDB | Python · GenAI · React |
| transaction-pipeline | Production financial transaction processing — deduplication, currency conversion, top spender analysis | Python |
| depytools | Production-grade Python, SQL & Spark patterns for Data Engineers + AWS DE cert prep | Python · SQL · Spark |
| service-humanity-website | Live website for a 43-year-old orphanage foundation in India | Next.js · TypeScript · Tailwind |
| Metric | Value |
|---|---|
| Users served | 2,200+ |
| Marketing budgets managed | $1.8B+ |
| Daily events processed | 10M+ |
| Pipeline availability | 99.9% |
| Technical interviews conducted | 33+ |
| Teams using my frameworks | 12+ |
