• 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.

• Define the target-state Azure data architecture (ingestion, orchestration, storage zones, serving patterns), security/networking boundaries, cost/perf tradeoffs, and promotion strategy (Dev→Test→Prod). • Implement robust ELT/ETL with ADF/Synapse Pipelines (parameters, reusable templates, CI/CD). • Hands-on in T-SQL and Python/PySpark for transformations, utilities, and tests. • Physical/semantic modeling, partitioning, columnstore strategies, statistics management, query plan analysis, index design, concurrency & transaction isolation, workload management. • SLA/SLO definitions, Azure Monitor / Log Analytics / App Insights dashboards and alerts; error handling, retries/backoff, idempotency, CDC and schema drift strategies. • RBAC, Key Vault, managed identities, private endpoints/VNet, data masking patterns; document data contracts and access patterns. • Code reviews, PR discipline, mentoring, and crisp documentation/runbooks for client handoff.

• Design and implement scalable, reliable, and efficient data pipelines to support clinical, operational, and business needs. • Develop and maintain architecture standards, reusable frameworks, and best practices across data engineering workflows. • Build automated systems for data ingestion, transformation, and orchestration leveraging cloud-native and open-source tools. • Optimize data storage and processing in data lakes and cloud data warehouses (Azure, Databricks). • Develop and monitor batch and streaming data processes to ensure data accuracy, consistency, and timeliness. • Maintain documentation and lineage tracking across datasets and pipelines to support transparency and governance. • Work cross-functionally with analysts, data scientists, software engineers, and business stakeholders to understand data requirements and deliver fit-for-purpose data solutions. • Review and refine work completed by other team members, ensuring quality and performance standards are met. • Provide technical mentorship to junior team members and collaborate with contractors and third-party vendors to extend engineering capacity. • Use Databricks, DBT, Azure Data Factory, and SQL to architect and deploy robust data engineering solutions. • Integrate APIs, structured/unstructured data sources, and third-party systems into centralized data platforms. • Evaluate and implement new technologies to enhance the scalability, observability, and automation of data operations. • Proactively suggest improvements to infrastructure, processes, and automation to improve system efficiency, reduce costs, and enhance performance.