Integrated Nielsen, Knobloch, and internal datasets using Python and SQL, applying validation rules to improve consistency, traceability, and availability of retail data. Automated commercial metrics including SOG, SOM, growth, and opportunity indicators across SKU, channel, and time levels to support business analysis. Created analytical outputs and executive-level reports with Dataiku and Power BI to support business decision-making. Processed and managed large-scale datasets in Azure and BigQuery, resolving data-quality and integration issues and developing transformation and enrichment processes. Built reusable Element workflows and automated recurring data processing and reporting through parameterized Jupyter pipelines orchestrated with Papermill.

Built data pipelines connecting PostgreSQL and Google Sheets, and automated dashboards in Looker Studio and Tableau. Developed analytical models in Python and R, including regression, time-series forecasting, clustering, and natural language processing. Analyzed website and campaign performance, identifying user behavior patterns and translating findings into actionable recommendations. Collaborated with marketing and development teams on event tracking and conversion measurement in GA4 and GTM; investigated reporting discrepancies across these tools and Adobe Analytics and coordinated corrections.

Evaluated AI-generated outputs and datasets against defined quality and consistency criteria. Identified recurring errors and documented improvement opportunities to increase AI system reliability.