• Design, develop, and maintain a scalable data warehouse/lakehouse environment. • Design and implement ELT pipelines to ingest, transform, and deliver high-quality data for analytics and reporting, incorporating current best practices, such as “pipelines as code”. • Ensure data security and compliance, including role-based access controls for security, encryption, masking, and governance best practices to ensure compliant handling of sensitive information. • Optimize performance of data workflows and storage for cost efficiency and speed. • Partner with engineers, analysts, and stakeholders to meet data needs; balance cost, performance, simplicity, and time-to-value while mentoring teams and documenting standards. • Define and implement robust testing frameworks, enforce data contracts, and establish observability practices including lineage tracking, SLAs/SLOs, and incident response runbooks to maintain data integrity and trustworthiness. • Monitor, troubleshoot, and resolve data & automation issues. • Collaborate within an Agile-Scrum framework and develop comprehensive technical design documentation to ensure efficient and successful delivery. • Serve as a trusted expert on organizational data domains, processes, and best practices.

• Design, build, and maintain robust ETL/ELT data pipelines for batch and streaming workloads. • Implement data ingestion and transformation workflows using Apache Airflow, Apache NiFi, Apache Spark, and Kafka. • Integrate data from multiple sources including REST APIs, files, relational databases, message queues, and external SaaS platforms. • Optimize pipelines for performance, scalability, reliability, and cost efficiency. • Develop and operate a centralized data integration hub that supports multiple upstream and downstream systems. • Build reusable, modular integration components and frameworks. • Ensure high availability, fault tolerance, and observability of data workflows. • Design and manage data warehouses, data lakes, and operational data stores using PostgreSQL and related technologies. • Implement appropriate data modeling strategies for analytical and operational use cases. • Manage schema evolution, metadata, and versioning. • Implement data validation, monitoring, and reconciliation mechanisms to ensure data accuracy and completeness. • Enforce data security best practices, access controls, and compliance with internal governance policies. • Establish logging, alerting, and auditability across pipelines. • Automate data workflows, deployments, and operational processes to support scale and reliability. • Monitor pipelines proactively and troubleshoot production issues. • Improve CI/CD practices for data engineering workflows. • Work closely with data scientists, analysts, backend engineers, and business stakeholders to understand data requirements. • Translate business needs into technical data solutions.

• Design and develop conceptual, logical, and physical data models for various domains • Lead the development of data modeling standards, best practices, and guidelines • Develop end-to-end solution architectures for data-driven and AI-focused applications • Mentor and guide junior data modelers • Design data models to support enterprise and operational reporting • Collaborate with data scientists to develop features for machine learning models • Optimize data models for performance and scalability • Ensure data models comply with data governance policies and standards • Identify and define data quality checks and validation processes • Implement data quality monitoring and reporting mechanisms

• 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.