• Pipeline Development and Optimization: Build and maintain reliable, scalable ETL/ELT pipelines using modern tools and best practices, ensuring efficient data flow for analytics and insights. • Data Modeling and Transformation: Design and implement effective data models that support business needs, enabling high-quality reporting and downstream analytics. • Collaboration Across Teams: Work closely with data analysts, product managers, and other engineers to understand data requirements and deliver solutions that meet the needs of the business. • Ensuring Data Quality: Develop and apply data quality checks, validation frameworks, and monitoring to ensure the consistency, accuracy, and reliability of data. • Performance and Efficiency: Identify and address performance issues in pipelines, queries, and data storage. Suggest and implement optimizations that enhance speed and reliability. • Security and Compliance: Follow data security best practices and ensure pipelines are built to meet data privacy and compliance standards. • Innovation and Continuous Improvement: Test new tools and approaches by building Proof of Concepts (PoCs) and conducting performance benchmarks to find the best solutions. • Automation and CI/CD Practices: Contribute to the development of robust CI/CD pipelines (GitLab CI or similar) for data workflows, supporting automated testing and deployment.

• Pipeline Development and Optimization: Build and maintain reliable, scalable ETL/ELT pipelines using modern tools and best practices, ensuring efficient data flow for analytics and insights. • Data Modeling and Transformation: Design and implement effective data models that support business needs, enabling high-quality reporting and downstream analytics. • Collaboration Across Teams: Work closely with data analysts, product managers, and other engineers to understand data requirements and deliver solutions that meet the needs of the business. • Ensuring Data Quality: Develop and apply data quality checks, validation frameworks, and monitoring to ensure the consistency, accuracy, and reliability of data. • Performance and Efficiency: Identify and address performance issues in pipelines, queries, and data storage. Suggest and implement optimizations that enhance speed and reliability. • Security and Compliance: Follow data security best practices and ensure pipelines are built to meet data privacy and compliance standards. • Innovation and Continuous Improvement: Test new tools and approaches by building Proof of Concepts (PoCs) and conducting performance benchmarks to find the best solutions. • Automation and CI/CD Practices: Contribute to the development of robust CI/CD pipelines (GitLab CI or similar) for data workflows, supporting automated testing and deployment.

• Design and improve the cloud structure (AWS, Azure, or GCP) of our data platform, ensuring it works well for many clients at the same time. • Build a system ready for failures, capable of recovering automatically and keeping things running if there are issues in production. • Create clear APIs, templates, and common tools so internal developers can write code faster and with fewer mistakes. • Monitor and optimize the use of cloud servers and tools (like Kubernetes) to keep the product cost-effective to run. • Identify parts of the system that have become outdated and plan how to improve them step-by-step without stopping daily work. • Review team designs and define the company's technical standards, helping to resolve questions without becoming a bottleneck. • Ensure the platform's structure inherently protects customer data and complies with security rules from the very beginning. • Decide how we measure platform performance so that if something fails, we know exactly what happened within a few minutes. • Build quick prototypes and test new tools before deciding whether to adopt them company-wide.

• Definir la estrategia analítica y el modelo funcional de la solución, traduciendo objetivos de negocio en decisiones de arquitectura. • Diseñar y construir el pipeline de ingesta y transformación sobre AWS: funciones Lambda (integradas con Microsoft Graph API), Data Lake en S3, jobs de Glue y tablas externas en Athena. • Configurar la seguridad de la infraestructura (IAM) y, según corresponda, su capa de red, asegurando que la arquitectura cumpla los estándares exigidos en entornos regulados como el financiero. • Diseñar el modelo dimensional (star schema) pensado para escalar a nuevas entidades sin rediseño, y el modelo de seguridad a nivel de datos (RLS), incluyendo su mapeo a la identidad corporativa del cliente. • Definir los estándares de arquitectura, métricas de negocio y consultas analíticas que el equipo de desarrollo ejecuta. • Actuar como autoridad técnica frente al cliente: validar la calidad de los entregables del equipo y ser el punto de contacto para decisiones de arquitectura e infraestructura. • Mantener la documentación de arquitectura como base de gobierno y transferencia de conocimiento.