Built and maitained ETL/ELT pipelines, moving data from application databases to data lakes and warehouses.Designed and got stakeholders engaged in building a data stack with modern tools, capable of handling our BI reporting and Data Science needs.Developed a data platform from scratch, with a multi-tenant Lakehouse and open source tools (Dagster, Airbyte, Spark/Trino, dbt, Iceberg, Minio, Metabase, Docker).Advanced efforts in data governance and data quality, promoting better cooperation between data producers and consumers.

Developed and maintained Recommender Systems for Retail handling large-scale clients (20+ million clients).Co-advised PhD two projects with scholarships financed by Neos, in relevant applications for retail data as recommendation systems, omnichannel marketing, customer segmentation, campaign optimization, synthetic data generation and large language models.Helped identify business oportunities from research projects and prototype new data products with various tools (Flask, FastAPI, BentoML, LangChain, TensorFlow, Keras, PyTorch).