• 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.

• Support the execution of Power BI projects, working alongside expert Principal Consultants and Solution Architects. • Create Data Storage Solutions with SQL Server and Data Lakes. • Develop ETL Pipelines with Azure Data Factory. • Provision Azure Subscriptions and Resources. • Develop Automation Solutions using languages such as PowerShell and Python

• Design and implement scalable data platform architecture on Databricks, supporting both batch and streaming ingestion • Build robust, fault-tolerant data ingestion pipelines that integrate with multiple third-party APIs and data providers • Design and implement AI-powered enrichment stages within pipelines—applying ML clustering, generative AI summarization, classification, and entity extraction to transform raw data into actionable intelligence • Build analytical systems with full-text search capabilities using Elasticsearch for rapid querying and analysis of enriched data • Work with AI/ML researchers to implement, integrate and scaling AI processing • Expose data platform capabilities as APIs and other interfaces for downstream consumption by applications and services • Optimize data lake and lakehouse architecture for performance, cost-efficiency, and scalability • Design and implement data quality frameworks, monitoring, and alerting systems • Design efficient architectures for calling external AI APIs and managing rate limits, costs, and reliability • Architect solutions with cost-efficiency as a first-class concern, implementing monitoring and optimization strategies for compute and storage • Make critical build-vs-buy decisions and establish architectural standards for the data organization • Mentor engineers and elevate the team's technical capabilities through code reviews, design discussions, and knowledge sharing