• Ownership de la capa analítica • Diseñar y mantener modelos de datos analíticos en DBT, asegurando la trazabilidad, eficiencia y calidad de los datasets. • Construir y optimizar consultas complejas mediante SQL en BigQuery. • Gestionar la creación de tablas, vistas, rutinas y queries programadas (scheduled queries) para asegurar la disponibilidad y calidad de los datos. • Definir, estandarizar y documentar métricas y KPIs para garantizar su consistencia en toda la organización. • Colaborar con los equipos de negocio para entender necesidades analíticas y traducirlas en modelos de datos escalables y reutilizables. • Desarrollar y mantener dashboards y visualizaciones en Metabase y Tableau, asegurando su alineación con los modelos analíticos. • Implementar procesos de testing y validación de datos (data quality) dentro de DBT. • Participar en la definición de buenas prácticas de versionado, testing y documentación en Git. • Contribuir a la mejora continua de la arquitectura de datos junto al Senior Data Engineer.

• Own the end-to-end data stack—from ingestion and warehousing to modeling and BI. • Maintain a warehouse (Snowflake) and connect it to source systems (EMR, RCM, patient engagement, scheduling, support tools). • Implement ELT (currently using Meltano) (plus custom connectors when needed) and orchestration (dbt Cloud/CI). • Own company data strategy, detailed architecture and design of replica, warehouse, and BI tool. • Build curated marts and a governed semantic layer in dbt; define durable metrics (e.g., Time-to-Care, referral funnels, cancellations, provider capacity, cohort outcomes). • Add data quality tests (dbt tests/Great Expectations), lineage, and alerts; resolve root causes quickly. • Administer BI tool (currently Sigma), define roles/permissions, and ship high-leverage dashboards. • Drive stakeholder discovery; translate questions into metrics, dashboards, and data contracts. • Train teams on self-serve best practices and documentation. • Implement HIPAA-aligned controls: RBAC/ABAC, column-level masking/tokenization, audit logging, data retention, and least-privilege access. • Monitor performance, freshness, and cost (warehouse, ELT, BI); optimize with SLAs for priority datasets.

• Design, develop, and maintain ETL/ELT pipelines for data integration from multiple source systems • Build and enhance data warehouses, data marts, and analytics platforms • Perform data extraction, transformation, cleansing, and loading to ensure data quality and accuracy • Optimize SQL queries, ETL jobs, and data processing performance • Support data modeling (conceptual, logical, physical) and metadata management • Develop and maintain data integration workflows and automation processes • Conduct data profiling, validation, and testing (unit, integration, performance, data quality) • Collaborate with data architects, BI developers, business analysts, and QA teams • Provide production support, troubleshoot issues, and implement fixes/enhancements • Ensure compliance with data governance, security, and federal regulations (FISMA, NIST, FedRAMP) • Contribute to documentation, SOPs, and knowledge transfer activities • Provide technical leadership and enforce best practices

• You'll own the dbt project end to end • You'll own the semantic layer • You'll own data observability • You'll own the orchestration layer • Collaborate with clinical, operations, and finance stakeholders on metric definitions and data quality