Analytics Engineer
I design and build production data systems. Pipelines, APIs, models, and interactive products that turn data into decisions.
Before engineering data, I ran a business. That experience shapes every system I build — from understanding the real problem to shipping something that works.
Map the business context, identify the decision that needs better information, and define what success looks like.
Design the data architecture. Source ingestion, transformation logic, storage, and access patterns before writing production code.
Implement pipelines, APIs, and interfaces. Prioritize reliability — tests at every layer, validation at every boundary.
Validate output against the original business problem. A pipeline that runs is not the same as a pipeline that delivers value.
I ran a business before I built data systems. Managing inventory, costs, logistics, and margins taught me that data is only valuable when it helps someone make a better decision. That principle guides every system I design.
Read more →Enterprise climate intelligence platform integrating 5 data sources with real-time monitoring.
ML-driven hotel revenue management achieving +30% net revenue improvement.
Multi-market logistics intelligence platform with ML cost prediction achieving R² = 0.987.
EU telecom network stress simulation validating 5/6 hypotheses on market dynamics.
I'm open to Analytics Engineer opportunities where engineering quality and business context matter.