Teams at OREDATA

Manage large-scale banking IT projects focused on data governance, data quality, data management, Data Warehousing, and regulatory reporting. Coordinate technical and business stakeholders, oversee planning and delivery, track progress, manage risks and dependencies, and ensure alignment with data standards, governance frameworks, and regulatory requirements.

Designs, develops, and optimizes production machine learning models for customer segmentation, recommendation systems, and revenue or demand forecasting. Processes large-scale datasets with Spark and PySpark, performs feature engineering and model evaluation, and supports model monitoring and automated retraining. Collaborates with data engineering and platform teams to integrate machine learning solutions into production environments.

Design and document enterprise data architecture (conceptual, logical, physical), select storage solutions (warehouses, lakes, ODS), build ETL pipelines, ensure data quality and security, evaluate new data technologies, and collaborate with data engineers, scientists, and analysts to meet business data needs.