Data Platforms
Designing Azure- and Databricks-based platforms that make analytics and AI delivery operational, not improvised.
About
Designing data platforms that make AI operational
Senior Data Engineer and Data Architect at Datumo, focused on building production-grade Data and AI solutions on Microsoft Azure.
My work centers on making AI useful in real environments: platform architecture, repeatable delivery, MLOps foundations, observability and the engineering boundaries between data systems and machine learning systems.
Azure-native data platforms and delivery systemsDatabricks, lakehouse patterns and analytics-ready architectureMLOps foundations for repeatable delivery and model operationsData engineering connected to production ML and AI systems
I care about systems that are scalable, observable, maintainable and actually ready for production, not only persuasive in a workshop or notebook demo.
What I build
Three recurring problem spaces
The common thread is operational reality: architecture, delivery and platform quality need to hold together under production constraints.
MLOps Systems
Building model lifecycle foundations, delivery workflows, observability and reliability around production ML.
AI in Production
Treating model quality and platform quality as the same engineering problem, especially in edge, IoT and data-centric workloads.
Principles
How I tend to reason about systems
01
Prefer systems that can be operated by teams, not only by their creators.
02
Bias toward reproducibility, observability and explicit trade-offs.
03
Treat notebooks as a useful beginning, not a deployment strategy.
Beyond work
Interests that map well to engineering
Animal lover with a soft spot for real-world chaos over perfect lab conditions.
Gaming enthusiast, especially where systems, strategy and immersion matter.
DIY hobbyist who likes building, fixing and understanding things end to end.