Andrés Vazquez Aviña

Design and maintain scalable data pipelines in production on AWS, supporting the integration of ML models for cybersecurity use cases — from raw data ingestion through to clean, validated datasets ready for model consumption. Implement data quality controls and validation rules to guarantee accuracy, reliability, and compliance with Airbus security and data governance standards. Map and structure data flows between IT and operational teams; define and document data architecture decisions to align with global IT standards. Translate technical data requirements from cross-functional stakeholders into clean, accessible datasets; provide technical support and guidance on data-related matters across teams.

Designed and delivered an end-to-end ETL pipeline on Databricks (Apache Spark) to ingest, clean, harmonize, and stage marketing and campaign data across LATAM markets, enabling downstream analytics and reporting. Defined the data architecture and lifecycle management strategy for LATAM data domains: ingestion, transformation, quality checks, storage, and utilization for AI and BI use cases. Implemented data quality rules and validation frameworks to ensure data accuracy and reliability at every pipeline stage; coordinated with local teams to maintain data sources current and compliant. Contributed to high-value use cases: ML pricing model development (feature engineering, dataset preparation), Power BI dashboards, and automated reporting infrastructure. Collaborated closely with data scientists to translate modelling requirements into structured, production-ready datasets and reusable data products.