• Liderar la ejecución técnica y escalar la infraestructura de datos. • Actuar como mentor de un equipo de Data Engineers y Analytics Engineers. • Colaborar con equipos de pagos y atención al cliente. • Supervisar el ciclo de vida de la información desde la ingesta hasta el consumo. • Optimizar rendimiento y eficiencia de costos en el Data Warehouse. • Implementar políticas de gobierno de datos.

• Implement data integration architecture and solutions for our Investments and Hedging delivery teams. • Design and develop ETL logic using Informatica Informatica Data Management Cloud, Snowflake and various Azure data tools to support existing and future deployments • Design and execute technical aspects of data management functions including creating, loading, transforming, cleansing, processing, analyzing, and visualizing data • Collaborate directly with teammates and business partners to build technical solutions that solve problems, are reusable, scalable, fast, and maintainable through large datasets, both clean and un-clean • Work with multiple development technologies including SQL • Interact cross-functionally with other technical teams both internally and externally to implement complete technology solutions • Design and develop code to support existing and future deployments • Independently solve problems and fix technical issues • Responsible for unit test construction and execution

• Architect and evolve scalable data ingestion and egress frameworks and pipelines that are well tested and offer strong data quality monitoring. • Architect and evolve our CI/CD processes - enhancing the testing environment and observability (such as building LLM-driven reviews with context awareness through data diffing, lineage analysis / downstream impact analysis, and general context). • Architect delivery architecture of data assets to external team partners to reduce manual operational overhead associated with month end close. • Enhance our Claude Code / LLM development support capabilities - creating tools / skills / agents that give our LLMs more context and help us continually improve their abilities to debug, create code, and maintain systems. • Enhance our security posture in our AWS / Databricks environment. • Design and implement distributed data processing systems using Spark and Databricks on AWS. • Establish clear ingestion and integration boundaries that eliminate single points of failure. • Proactively surface risks, dependencies, and tradeoffs before they impact delivery. • Produce clear technical artifacts and recommendations for stakeholders and leadership. • Design logical and physical data models balancing flexibility, performance, governance, and scalability. • Partner closely with the Analytics Engineers on the Finance Data Team to support high-quality downstream data modeling & reporting. • Harden pipelines with monitoring, alerting, SLAs, and recovery mechanisms. • Mentor engineers and elevate distributed systems rigor across the team.

• Lead the design and implementation of data components • Ensure data synchronization across all necessary systems • Maintain a high level of security to protect sensitive data • Align systems with legal and regulatory requirements