Vice President, Strategic Data Architecture

Datasite and its associated businesses are the global center for facilitating economic value creation for companies across the globe. From data rooms to AI deal sourcingand more. Here you’ll find the finest technological pioneers: Datasite, Blueflame AI, Grata, and Sherpany. They all, collectively, define the future for business growth.Apply for one position or as many as you like. Talent doesn’t always just go in one direction or fit in a single box. We’re happy to see whatever your superpower is and find the best place for it to flourish.Get started now, we look forward to meeting you..Job Description:The Vice President of Strategic Data Architecture (VP SDA) is a senior enterprise data leader responsible for defining, governing, and advancing the architectural vision for data across a federated group of companies. This role spans multiple business units and platforms and requires a leader who can unify disparate data environments into an integrated, scalable, and future ready enterprise data ecosystem. The VP SDA will shape the long-term data architecture strategy, establish modern data capabilities, and ensure that data systems are secure, high-quality, governed, and aligned with business priorities. This leader will oversee all data architecture disciplines, including enterprise medallion architecture, data Lakehouse, MDM, data systems and integration architecture, and AI-driven data capabilities, through a blend of direct oversight and dotted line architects across the group. Success in this role requires a strong strategic mindset, deep technical expertise, and the ability to translate complex architectural concepts into clear, business aligned roadmaps. The VP will provide thought leadership, guide cross company alignment, and drive the evolution toward a cohesive data operating model that supports data products, analytics, governance, and innovation and enterprise adoption of AI, Generative AI, and Agentic AI frameworks. This is a remote position based in the US.Key ResponsibilitiesEnterprise Architecture LeadershipEstablish and maintain the enterprise data architecture vision, including conceptual, logical and physical models, data flows, storage patterns, and integration frameworks. Create and maintain a comprehensive inventory of current state data capabilities across all business units. Define the target state architecture and align stakeholders on the required data product capabilities and technical foundations. Define and integrate already architectural patterns that support enterprise AI, Generative AI, and Agentic AI use cases, ensuring scalable and governed deployment across the ecosystem. Strategic Alignment and CollaborationPartner closely with leaders across Data Management, Data Governance, Data Product, Data Engineering, Analytics, and Product teams to translate business needs into data driven solutions. Serve as the primary liaison between Data Management and business units to ensure data strategies are fully aligned with organizational objectives. Collaborate with AI/ML teams to shape enterprise AI strategies, ensuring data architecture enables model development, model operations (MLOps), vector storage, retrieval augmented generation (RAG), and agent-oriented workflows. Data Ecosystem OptimizationIdentify opportunities to simplify, consolidate, or modernize legacy technologies, data stores, and applications. Oversee the design of scalable architectures that support enterprise-wide data products and shared capabilities across multiple business units and group-level functions. Monitor data and related AI platforms and systems to ensure availability, performance, reliability, and cost efficiency. Governance, Quality, and SecurityLead the development and enforcement of data architecture standards, quality frameworks, and governance policies. Collaborate with Legal, Compliance, and Information Security to ensure alignment with security, privacy, and regulatory requirements. Implement robust data security and access control models to protect sensitive data across the ecosystem. Incorporate AI governance standards, including model transparency, lineage, auditability, responsible AI practices, and controls for Generative AI and Agentic AI solutions. Innovation & Continuous ImprovementStay current with emerging technologies, architecture patterns, and industry best practices. Identify opportunities for innovation within cloud, big data, MDM, data lifecycle management, and product centric architectural approaches. Promote data architecture best practices across teams and business units. Provide thought leadership on emerging AI patterns, including vector databases, multimodal architectures, orchestration frameworks, agent based systems, and next generation data AI integration models. QualificationsEducationBachelor’s degree in Computer Science, Information Technology, Data Science, or a related field. Master’s degree or relevant professional certifications (e.g., CDMP, CIMP) st