Data Platform Engineer — real-time streaming analytics & cloud data warehousing

~5 years designing and running data systems for large-scale logistics operations.

• ⚡ Real-time streaming analytics — built and maintain a multi-use-case event-processing platform powering live operational dashboards, handling continuous high-volume streams under strict latency requirements.
• ☁️ Cloud data warehousing — architected a Snowflake-based data warehouse from the ground up: CDC ingestion (AWS DMS) → staging (RDS, S3) → compute (EC2), replacing a legacy on-premise setup.
• 📊 BI platform administration — full lifecycle ownership of an enterprise BI/visualization platform: server admin, dashboard delivery, and a complete on-premise-to-cloud migration.
• 🧰 Tooling & automation — Python utilities for data validation and internal alerting/notification workflows, packaged and distributed for reuse across teams.

• 🧱 Dimensional data modeling — Kimball methodology, all SCD types, medallion architecture
• 🔒 Pipeline correctness at scale — idempotency, backfills, quality gates, audit logging
• 📈 The gap between "works in a demo" and "works at 100x" — partitioning, file sizing, compaction, orchestration under load
• 🐘 SQL performance & dialect migration (Oracle → Snowflake) and the tradeoffs each warehouse forces
• 🏗️ System design for data platforms — batch vs streaming, schema evolution, contract-driven pipelines

A self-contained, hand-written shipment-tracking data pipeline as a public reference project — raw ingestion through a medallion architecture (🥉 bronze → 🥈 silver → 🥇 gold), dbt-modeled dimensional layer with a real SCD Type 2 implementation, orchestration, data quality gates, and a documented "what breaks at 100x" section.

Built without code generation — to demonstrate engineering judgment, not tool familiarity.

📡 Real-time / event-streaming systems  |  📊 Enterprise BI platforms  |  🗄️ AWS: DMS · RDS · S3 · EC2