• Lead data architecture efforts including logical and physical data modeling • Design scalable database schemas and enterprise data structures to support analytics and reporting • Define standards for data modeling, naming conventions, and data quality practices • Support enterprise reporting needs by enabling clean, well-structured datasets and semantic layers • Partner with engineering and integration teams to align data design with system interfaces and workflows • Contribute to data governance, lineage, and documentation of key data entities and definitions • Optimize data models for performance, scalability, and maintainability • Provide architectural guidance for enterprise data warehouse and/or data lake implementations

• Cooperate on design and implementation of high-performance, scalable data solutions, including data lakes and streaming systems • Architect robust ETL/ELT pipelines and event-driven ingestion processes to ensure seamless data flow across PostgreSQL, Redshift, and DynamoDB • Optimize cloud resources for peak performance and cost-efficiency • Collaborate with cross-functional teams to translate business requirements into comprehensive, cloud-based solutions • Support data governance efforts, establishing policies that guarantee the highest standards of data quality, security, and compliance

• Build, lead, mentor, and inspire a high-performing, globally distributed data platform team • Define and execute the technical strategy and roadmap for the enterprise data platform, overseeing the end-to-end development lifecycle from architecture to deployment and operations • Manage hiring, performance, career development, and resource allocation to ensure the team delivers its commitments with excellence • Champion agile methodologies, a DevOps mindset, and a culture of operational excellence, defining and monitoring SLOs for all platform services • Partner with the central Data Governance team to implement technical solutions that enforce data policies, standards, and compliance requirements across the platform • Develop the scalable, foundational data services and self-service tools that empower Data Science and Analytics teams to work efficiently • Partner closely with key stakeholders in Data Science, Analytics, Product Management, and Application Engineering to understand their needs and ensure the data platform meets their requirements.

• Own the end-to-end design of Zócalo Health’s data platform, including warehouse, lake, and ingestion architecture • Build and operate production data pipelines across clinical, operational, and third-party systems (APIs, CDC, events) • Define core analytical and longitudinal data models used across the company • Build robust data ingestion frameworks, including Change Data Capture (CDC), API-based ingestion, and event or webhook-driven pipelines • Implement testing, monitoring, and observability practices to ensure data quality, pipeline reliability, and system performance • Apply strong engineering fundamentals to improve scalability, performance, and cost-efficiency of data systems • Partner with Product to ensure data systems support metric definitions, outcome measurement, and roadmap prioritization.