• Design and build scalable data pipelines to ingest, transform, and curate data from APIs, databases, files, and event streams. • Lead technical design reviews and translate complex business needs into enterprise-grade data solutions. • Develop and optimize advanced data models (dimensional, data vault, domain-driven, canonical) to support analytics, BI, and productized datasets. • Champion SDLC best practices, continuous delivery, and infrastructure automation using CI/CD and Infrastructure as Code. • Optimize complex distributed workloads using SQL, Python; mentor others on tuning and scalable design patterns. • Build reusable data frameworks, libraries, and reference architectures to accelerate team productivity and platform adoption. • Perform root-cause analysis for major data incidents, lead long-term remediation, and drive operational reliability improvements. • Provide technical mentorship, guide code reviews, and help shape engineering capability maturity. • Collaborate with Architects, Data Leads, Product Owners, and cross-functional engineering teams to define long-term data strategies. • Perform other duties as assigned.

• Define, evolve and disseminate the Data and Artificial Intelligence Reference Architecture, establishing standards, frameworks and best practices across the group. • Diagnose the current Data and AI landscape and propose scalable, resilient architectures aligned with business strategy. • Lead the evolution of the corporate data platform based on Databricks, ensuring scalability, governance and high availability. • Design and evolve the corporate AI Agents platform, promoting the use of Generative AI and AI-First architectures to accelerate solution development. • Define standards for ingestion, transformation, integration, modeling, quality, security, governance and data provisioning. • Implement and promote Data Mesh, Data as a Product and Federated Governance practices, enabling domain autonomy with shared corporate standards. • Design and develop reusable components, pipelines, models, AI agents, frameworks and accelerators to optimize squad delivery. • Structure and evolve a corporate marketplace for sharing data products, models, agents, components and reusable assets. • Provide technical leadership for the architecture and implementation of strategic Data and Artificial Intelligence initiatives, ensuring compliance with corporate guidelines. • Serve as a technical reference for Data Engineering, Data Science, Analytics and AI teams, promoting best practices, architectural reviews and continuous improvement. • Collaborate with Engineering, Cloud, Infrastructure, Security, Governance, Product and Business teams to build a modern, secure platform prepared for organizational growth. • Keep up with trends, technologies and best practices related to Data Architecture, Generative AI, MLOps, DataOps and Platform Engineering, fostering continuous innovation.

• Lead the design, development, and implementation of enterprise data architecture for the new platform • Architect scalable data platforms supporting daily operations, clinical management, reporting, and analytics • Leverage AI technology to design the system, develop the system, and maximize efficiency of operations • Design and maintain enterprise data models, including conceptual, logical, and physical data architectures • Define and implement data integration patterns across Microsoft Dynamics, EHR/EMR systems, claims systems, and third-party applications • Design and oversee ETL/ELT processes to ingest, transform, validate, and distribute data across enterprise systems • Establish data governance standards, metadata management practices, data quality frameworks, and master data management strategies • Collaborate with Engineering teams to design modern cloud-based data solutions leveraging Azure data services • Architect data lake, data warehouse, and data mart solutions to support reporting and advanced analytics needs • Ensure solutions comply with HIPAA, HITRUST, privacy, security, commercial, and regulatory requirements • Partner with business stakeholders to translate strategic objectives into scalable data solutions • Other duties as assigned

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers • Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume • Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables) • Implement data upgrade/migration paths from legacy schemas without breaking production • Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems • Establish data validation and integrity checks across the financial data layer • Collaborate with the Product Manager to translate finance domain requirements into durable schema designs • Document architectural decisions and data model contracts for downstream consumers