Projects
These are the ten projects that trace my path from a junior .NET developer on a Canadian bank's sales platform to a senior data engineer architecting multi-cloud identity resolution systems processing 800 million records. Each card below covers a specific engagement — the company, the tech stack, and what I actually built — click through for the full breakdown of highlights and outcomes.
The common thread across all of them is data movement at scale: ingesting messy files from external partners, cleaning and standardizing identity data, and orchestrating the pipelines that keep it flowing reliably in production. Early projects (2017–2020) were mostly C#/.NET microservices and SSIS packages for financial and retail clients at Capgemini and Deloitte. From 2020 onward, the work shifted toward Python, Apache Airflow, and Apache Spark running on GCP, AWS, and Azure — culminating in the identity resolution platform migration that cut processing time in half while scaling to hundreds of millions of records, and the Kubernetes-based autoscaling pattern (KEDA HTTP triggers) I helped pioneer for Deloitte's Customer Growth Opportunity platform in 2026.
Recurring themes worth calling out: cutting multi-day batch jobs down to hours (LGE's CDP pipeline went from 18–20 days to 2–3 days), containerizing legacy monoliths into microservices, and building the observability and testing infrastructure that makes those pipelines trustworthy in production rather than just fast in a demo.
For the full career timeline with certifications, awards, and colleague recommendations, see the About page. For write-ups on the specific tools and patterns used in these projects — Apache Airflow orchestration, Spark migrations, and multi-cloud deployment — see the blog.