• Develops and maintains scalable data pipelines and builds out new API integrations to support continuing increases in data volume and complexity. • Collaborates with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility and fostering data-driven decision making across the organization. • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies. • Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.

• Definir el enfoque global de arquitectura de datos dentro del entorno de trabajo del proyecto. • Desarrollar modelos de datos curados alineados con el modelo de información corporativo. • Diseñar el enfoque de integración tras las ingestas iniciales. • Participar en la definición del marco de calidad del dato. • Diseñar la capa de consumo para dar soporte analítico completo a las distintas áreas del negocio. • Colaborar con equipos multidisciplinares en entornos ágiles. • Aplicar buenas prácticas de seguridad, gobierno y gestión del dato en arquitecturas cloud. • Dar soporte técnico y orientación a equipos relacionados con ingeniería y analítica.

• Construir una carrera tan única como tú • Convertirte en la mejor versión de ti • Ayudar a EY a ser aún mejor • Crear una experiencia excepcional para ti y un mundo laboral mejor para todos

• Deep hands-on experience in one or more financial-services domains, such as public or private markets, syndicated lending, securities processing, counterparty or client lifecycle management, or regulatory reporting. • Strong understanding of end-to-end workflows relevant to those domains (e.g. trade lifecycle, processing, reporting, reconciliation), and how data moves through systems. • Experienced in building medium to long term technology roadmaps and strategy. • Experience integrating and operating AI /ML, or agent-enabled components as part of broader systems, with attention to reliability, explainability, operational controls, and failure modes. • Experience designing systems where data quality, lineage, controls, and auditability are required and visible, not implicit. • Able to reason about domain-driven failure modes (late data, breaks, mismatches, reconciliation issues, regulatory deadlines) and design systems that surface and handle them cleanly. • Acts as a domain-aware technical partner to segment leads and delivery teams. • Strong experience designing and building distributed, data-intensive, and event-driven systems in financial services or similarly regulated environments. • Hands-on experience producing detailed designs and reference implementations and working directly with teams to turn designs into running systems. • Strong judgment around system behaviour in production, including performance, failure modes, resilience, recovery, and risk isolation. • Practical experience with cloud platforms (AWS, Azure, GCP), including managed services, multi-tenant designs, and cost/performance trade-offs. • Hands on experience in reviewing, writing, and improving production code, and working closely with senior engineers on complex problems.