Online CV & Professional Experience

Data Analyst

R&D for BI and digital marketing

LaMagnetica

June 2025 – November 2025 | Barcelona, Spain

Impact Summary

Comprehensive optimization of data infrastructure and digital analytics, achieving a 93% reduction in processing volume and a 10x speed improvement in critical queries. Design of scalable architectures on Google Cloud Platform and advanced custom visualizations for business dashboards.

Project Details and Key Achievements

• BigQuery Architecture Optimization: Reduction of processed data volume through SQL query refactoring and implementation of table partitioning strategies, achieving up to 93% load reduction.
• Performance Tuning: Acceleration of complex query response times up to 10x, ensuring stability and fast loading of Looker Studio dashboards for the end client.
• Visualization Engineering: Development of custom visual components (e.g. Lipstick Plot / Bullet Chart) using D3.js integrated into Power BI, allowing effective representation of complex KPIs compared against business objectives.
• Data Quality and Integrity: Detected critical data gaps impacting session coherence. Engineered a solution to inject synthetic 'page_view' events to maintain session continuity, resulting in an 8% recovery of missing events for users who accepted cookies.
• Scientific Validation: Designed and implemented a comprehensive testing protocol. Established the first objective data fidelity KPIs to measure the success of session reconstruction for cookieless users.
• Operational Efficiency: Drastic reduction of operational costs (~97%) through query optimization and automation of daily update processes.

Tech Stack Applied

Google BigQuery, SQL (Standard & Legacy), Looker Studio, Power BI (DAX), D3.js / JavaScript, Python (Automation).

Postdoctoral Researcher

Mathematics of Planet Earth Group

University of Reading | United Kingdom

April 2023 – February 2024

Impact Summary

Research focused on climate "Tipping Points" using stochastic systems, including the development of algorithms for early warning signals in noisy time series and the evaluation of policy impacts via numerical simulations.

Project Details and Key Achievements

• Climate Tipping Points: Conducted research on tipping points using stochastic systems to understand critical transitions.
• Early Warning Algorithms: Developed algorithms in Python and Matlab to detect early warning signals in noisy time series data.
• Policy Impact Evaluation: Performed numerical simulations to assess the impact of various environmental policies.
• International Collaboration: Collaborated with international teams under the TIPES project (EU Horizon 2020).

Tech Stack Applied

Python, Matlab, Stochastic Modeling, Numerical Simulations, Time Series Analysis.

PhD Researcher & Teaching Assistant

Nonlinear Dynamics & Climate Group

University of Geneva | Geneva, Switzerland

May 2018 – February 2023

Impact Summary

Conducted multiscale modeling of gravity waves and fluids, designed real-time analysis pipelines for experimental data, and validated theoretical models against experimental results. Published 4 peer-reviewed articles.

Project Details and Key Achievements

• Multiscale Modeling: Modeled gravity waves and fluids over complex bathymetries.
• Real-time Experimentation Pipeline: Designed a pipeline at the University of Sydney to analyze water tank measurement files in real-time, accelerating the simulation-to-experiment cycle.
• Experimental Validation: Validated high-intensity laser spectra statistics using a custom Python analysis pipeline.
• Model Validation: Used experimental data to initialize theoretical simulations, providing rigorous model validation beyond visual comparison.
• Academic Output: Published 4 articles in high-impact peer-reviewed journals.
• Mentorship: Supervised a Master's student.

Tech Stack Applied

Python, Fluid Dynamics, Wave Modeling, Data Analysis, Real-time Processing.