• Design, develop, and maintain scalable data pipelines focused on ingestion via CDC (Change Data Capture) using Oracle GoldenGate; • Configure and manage real-time and near-real-time data replication between source systems and cloud environments; • Ensure data consistency, integrity, and synchronization between source and target systems; • Monitor ingestion pipelines, perform troubleshooting, and optimize CDC process performance; • Support full and incremental (delta) load strategies; • Develop and maintain data processing pipelines using Azure and Databricks (Spark); • Implement transformations following modern data architecture patterns using Bronze, Silver, and Gold layers; • Optimize pipelines for performance, scalability, and cost efficiency; • Work with structured and semi-structured data for analytical consumption, reporting, and AI/ML initiatives; • Collaborate with data architects to define modern Lakehouse architectures; • Support data governance, data catalog, lineage, and compliance initiatives; • Ensure data availability, reliability, security, and quality for downstream consumption.

• As part of the Data Engineering team, you will be responsible for design, development and operations of large-scale data systems operating at petabytes scale. • You will be focusing on real-time data pipelines, streaming analytics, distributed big data and machine learning infrastructure. • You will interact with the engineers, product managers, BI developers and architects to provide scalable robust technical solutions.

• Diseñar y desarrollar soluciones de datos alineadas con la visión de Arquitectura de Datos. • Crear y optimizar ETLs, ELTs y APIs para manejo eficiente de datos. • Implementar estrategias de testing para validar calidad funcional y no funcional. • Proponer mejoras continuas en procesos y productos de datos. • Resolver incidencias técnicas y documentar soluciones conforme a estándares. • Mentorizar y apoyar el onboarding de nuevos integrantes del equipo.

• Design, build, and optimize end-to-end ETL pipelines for legal data from multiple jurisdictions, including cleaning, transformation, chunking, validation, embedding, and ingestion into vector databases • Work extensively with XML-based legal data feeds: parse, validate, normalize, and transform XML structures into scalable internal schemas and unified document formats • Develop and maintain data models and storage schemas that support continuously updated datasets while ensuring consistency, scalability, and accuracy across diverse datasets and large amounts of data • Coordinate data handover and integration from multiple internal and external data providers, including official sources, APIs, and web scraping pipelines, ensuring reliable and timely updates • Implement and continuously refine metadata enrichment strategies to maximize searchability, ranking quality, and relevance of legal information in vector databases. • Build and maintain a high-performance search and retrieval infrastructure enabling agent-based systems to call search functions and retrieve the most relevant legal information efficiently • Collaborate with product, AI, and legal domain experts to deliver high-quality, reliable data solutions • Own the data integration of one jurisdiction end-to-end