Welcome.
I create data solutions that are tailored to your needs, robust, and human-centered, balancing creativity, efficiency, and cost-conscious design. Let’s build clarity together.
Clarity Engineered.
Portfolio simulation of large-scale Spark performance engineering, demonstrating advanced audit, benchmarking, and optimization techniques using metrics, caching strategies, and lineage-aware diagnostics.
Neo4j project that enriches transactions into nodes/edges to expose rings and shared-entity patterns invisible to relational models.
Iterative PySpark pipeline with validated transforms that turns noisy payments data into analytics-ready features for risk scoring.
Time-series models on trading signals with evaluation on drift, slippage, and decision support, not just accuracy.
Simulating and analyzing latency arbitrage strategies in trading environments, focusing on order routing, microstructure, and execution dynamics
CiviSight: Turning Data Into Insights
An AI-driven platform that brings clarity to public discourse by aggregating, analyzing, and visualizing political speech, sentiment, and behavior.
Facial Expression Recognition
Compact CV model for emotion classification; shows data prep, augmentation, and deploy-ready inference.
Facial Expression Recognition
Compact CV model for emotion classification; shows data prep, augmentation, and deploy-ready inference.
GlassBox AI: making AI Transparent
A conceptual project on building AI systems that are inherently transparent and interpretable, embedding accountability and trust directly into the model design.
Gabriella
MANSUR
I’m Gabriella, a Data Engineer and continuous learner, always exploring new technologies and keeping up with the market. I’m a Microsoft Certified Data Engineer and currently preparing for the Databricks Data Engineer Associate exam. Alongside this, I bring hands-on experience across data engineering, analytics, data science, and product management.
My trajectory began in mechanical and petroleum engineering, where I focused on computational modeling and simulation of fluid flow. This work meant designing algorithms to solve complex systems of equations, optimizing simulations for stability and efficiency, and applying the principles of computational physics to model real-world phenomena with precision. This foundation in problem-solving and optimization naturally bridged into the world of data. Since moving to the Netherlands in 2015 to pursue my Master’s at TU Delft, I’ve continued my academic journey with an Engineering Doctorate (EngD) in Data Science at TU Eindhoven / JADS, building a career at the intersection of engineering, computation, and data.
What drives me is clarity. In a world where AI and over-engineered tools are marketed as the solution to everything, I start with something simpler: the real needs of the business and the people who will actually use the system. Sometimes the right answer is advanced, sometimes it is straightforward; but it should always be robust, efficient, and designed around those who depend on it.
For me, data engineering is ultimately about trust. Trust that the systems are reliable. Trust that decision-makers have clarity, not noise. Trust that solutions grow with the organization instead of holding it back.
My approach is human-centred and collaborative: listening first, understanding the context, and then building together. Rather than just delivering pipelines or dashboards, I create solutions that feel natural to adopt, empower teams with confidence, and bring lasting clarity and value to the organization.
Outside of work, you’ll usually find me moving or making. I love snowboarding, skateboarding, and tennis, as much as I enjoy slowing down in nature, gardening, and farming. Creative expression is central to me too, through piano, painting, design, and arts and crafts. I’m also into reading, board games, and chess, where strategy meets social connection. And of course, I share my days with my two Ragdoll cats, Tom and Mia, who keep life playful and curious.