• Design and develop end-to-end solutions using SAP Analytics Cloud platform, including data modeling, and visualization (knowledge of planning, predictive analytics, and collaborative features is a significant advantage) • Design and implement data models and connections within SAP Analytics Cloud (SAC), ensuring data integrity, performance, and security • Perform testing, debugging, and optimization of SAC applications to ensure quality, performance, and usability standards are met • Design of new reports, interactive Business Intelligence artifacts, or data visualizations • Refinement of data analysis work products developed by other HHS personnel • Migration of data analysis work products from one reporting software tool to another, such as from Crystal Reports to Business Objects • Development of new, or modification of existing, metadata describing data structures and encodings to facilitate end-user discoverability and understanding • Development of new, or modification of existing, aggregations to facilitate analytic performance improvements • Definition of new, or modification of existing, roles controlling access to data analysis work products • Develop, update, integrate APIs/Restful APIs as deliverables for enterprise initiatives and projects • Packaging of data analysis work products for deployment to test and production environments • Analysis and resolution of defects identified in data analysis work products • Create or modify documentation related to data analysis work products • Create or modify training material related to data analysis work products

• Design, develop, and maintain high-quality dashboards and reports in Power BI tailored to business needs. • Perform efficient data queries and modeling in SQL and Databricks, ensuring data integrity and performance. • Collaborate on data integration and governance processes across multiple data sources. • Interact with sales, operations, and support teams to understand their processes and translate them into actionable BI solutions. • Support automation and optimization initiatives for reporting to minimize operational overhead. • Document data processes, business definitions, and reporting best practices.

• 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.