VP, Enterprise Data Architect

The VP, Enterprise Data Architect 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 Responsibilities 

Enterprise Architecture Leadership 

• Establish 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 Collaboration 

• Partner 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 Optimization 

• Identify 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 Security 

• Lead 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 Improvement 

• Stay 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. 

Qualifications 

Education 

• Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field. 
• Master’s degree or relevant professional certifications (e.g., CDMP, CIMP) strongly preferred. 

Experience 

• 10+ years of experience in data management, data governance, or related disciplines within SaaS or cloud based environments. 
• Extensive background in enterprise data architecture, cloud solutions, data modeling, data warehousing, data lakes, data marts, and BI ecosystems. 
• Proven success designing and implementing architectures that deliver data as a product for internal and external stakeholders. 
• Strong understanding of structured and unstructured data management, data integration, data lifecycle management, and modern data platforms. 
• Experience working with Scaled Agile or similar methodologies. 

Skills 

• Proficiency in AI architectural patterns, data preparation for AI/ML, and designing systems that support LLMs and Agentic AI workflows.
• Expertise in cloud data platforms and tools (e.g., AWS, Snowflake, Redshift, Azure, Google Cloud, MDM, ETL/ELT tools). 
• Exceptional communication skills with the ability to influence technical and nontechnical stakeholders. 
• Deep knowledge of data governance frameworks, data management principles, and industry standards (ISO 27001, SOC 2). 
• Strong understanding of data privacy and compliance requirements (GDPR, CCPA, emerging AI related). 
• Advanced analytical, problem-solving, organizational, and system design skills. 

Preferred 

• Familiarity with project management tools and methodologies. 
• Experience with analytics, BI platforms, or data visualization tools.
• Knowledge of Virtual Data Room (VDR) security protocols and data protection standards. 
• Hands-on experience and Demonstrated leadership in developing AI ready platforms and guiding enterprise adoption of AI/Gen AI technologies.  

Work Location & Flexibility:

This position can be located remotely however if the candidate is based in our Minneapolis or New York City locations the office follows a hybrid work model. Employees in these locations are expected to work on-site a minimum of two days per week. Please note that specific business units or role requirements may necessitate additional in-office days based on team collaboration needs or operational priorities.