Collecting, consolidating and archiving organizational data, creating dashboards, and managing decision-support projects, including dimensional modeling (star and snowflake schemas).
Designing scalable database solutions across relational and alternative paradigms, and building ETL data pipelines with automated quality validation, governance, and security.
Building an end-to-end data science pipeline: data exploration, preprocessing, model training and evaluation, and natural language processing applications, using Python (Pandas, NumPy, Scikit-learn).
Visualizing data and crafting narratives with it, turning data into information that supports decision-making, based on Cole Nussbaumer Knaflic's Storytelling with Data.
Database management systems, relational model design and manipulation: SQL, transactions, normalization theory, conceptual modeling (UML), and object-relational persistence.