I’m an analytics engineer with over a decade of experience working with data end-to-end — from building pipelines and structuring datasets to modeling, analyzing, and making them genuinely useful.
I spent four years in Bordeaux, which gave me the chance to work abroad, see things differently, and pick up an appreciation for good French wine along the way.
I enjoy turning messy, real-world data into clean, reliable systems that people can actually use. Most of my work sits at the intersection of engineering and analytics — designing data models, improving data quality, and helping teams get consistent answers without having to second-guess the numbers.
My background in science (University of Waterloo) still shapes how I approach problems: stay curious, test assumptions, and keep things grounded in reality. Over time, I’ve worked across a range of industries and data environments, which has made me comfortable dealing with complexity without overcomplicating the solution.
Technical experience includes:
I work mostly in Python and SQL, building and maintaining data pipelines, models, and analytics layers.
Core
Python (pandas, NumPy, PySpark, scikit-learn)
SQL (data modeling, performance optimization)
dbt (data transformation, modeling workflows)
Data & Infrastructure
AWS, Hadoop ecosystem
Elasticsearch, NoSQL systems
Visualization & Reporting
Tableau, Kibana
Matplotlib
Other
Flask, JavaScript, HTML/CSS
R, Excel/VBA (when needed)