Building scalable ETL pipelines, data lakes, and distributed systems on AWS. Focused on cloud-native data platforms, analytics engineering, and FinOps-aware architectures.
01 — Expertise & Tech Stack
Core capabilities across AWS data services, distributed processing, and cloud-native engineering — focused on building reliable, cost-aware data platforms.
Data lake
Warehouse
ETL / Catalog
Compute
Ad-hoc query
Streaming
Orchestration
Big data / Spark
BI dashboards
Monitoring
Lake governance
ML platform
cost-aware design
Storage tiering · query optimization · right-sized compute
M.S. CS
Big Data Science · ongoing
02 — Featured Projects
Operational data systems and analytical deep-dives — each ships with reproducible infrastructure or a documented query pipeline.
End-to-end fraud detection workflow with a relational backbone and an interactive triage UI. Containerized for reproducible local and cloud deployment.
Exploratory analysis of a Kaggle dataset projecting AI's impact on the labor market through 2030. SQL-driven aggregation surfaces sector-level shifts and exposure trends.
An automated ELT/ETL pipeline optimizing raw customer review metrics via a decoupled serverless compute layer and low-latency storage orchestration.
03 — Publications
Long-form notes on the patterns I rely on day-to-day — built from real implementation experience, not just documentation.