• Análisis, diseño y desarrollo de soluciones de ingeniería de datos en entornos cloud. • Construcción y mantenimiento de pipelines de datos. • Tratamiento, integración y análisis de grandes volúmenes de información. • Optimización de procesos de datos en plataformas Microsoft Azure. • Data engineering y data analysis.

• Own and improve data orchestration patterns (Scheduling, Retries, Backfills). • Strengthen deployment and operational patterns to reduce "firefighting" for internal teams. • Ensure data pipelines and models are production-grade and operationally sound. • Use Terraform to support infrastructure-as-code for data platform components. • Document patterns and decisions to ensure internal teams can operate independently.

• Join as a founding engineer on Reporting & Statements team • Design systems for every financial report and statement delivery • Build automated frameworks for accuracy and consistency in financial reporting

• Lead AI-Ready Data Platform architecture and delivery: Define and execute the technical strategy for a scalable AI-Ready data platform, including the patterns and capabilities needed to make data usable for AI use cases and interoperability. • Establish data architecture patterns: Create and evolve reference architectures, modeling standards, guardrails, and best practices for our foundational data platform, including medallion and lakehouse architecture, ingestion, normalization, data quality, and AI interoperability. • Oversee key platform projects: Contribute heavily to AI-Ready data platform initiatives and cross-product data architecture improvements, including data layer re-architecture for our SE product, schema modernization, and data model evolution. • Mentor and lead: Guide teams in delivering projects, fostering a mentorship culture, and ensuring adherence to high standards in data engineering practices, data modeling, data quality, and platform architecture. • Drive best practices: Collaborate with R&D groups to implement best practices for making data AI-Ready and securely interoperable, including data contracts, ingestion and normalization standards, and improving consistency and reuse across products. • Partner on data governance and security: Work with Security and product teams to define data classification, retention, tenant isolation, and access controls for AI-Ready datasets and data products. • Enable adoption through paved roads: Provide reference implementations and blueprints that make it easy for teams to produce data products and integrate with the AI-Ready platform. • Architect for data observability: Define and implement standards for data quality, lineage and traceability, data dictionary controls, freshness monitoring, and alerting, so data products are reliable and audit-ready.