Vice President, Data Architecture and Engineering; Remote

About The Role

The VP, Data Architecture and Engineering is accountable for the vision, strategy, and execution of the enterprise data platform and engineering capabilities that enable business growth, operational efficiency, and customer centric innovation. This leader partners closely with business and technology teams to elevate data into a durable and scalable enterprise asset. In alignment with the VP of A.I and Transformation, who defines enterprise A.I

strategy and prioritizes business use cases, this role owns the data architecture, platforms, and engineering foundations required to operationalize, scale, and reliably run A.I solutions in production across the enterprise.

• Define and own the data product and technical data science strategy, aligning with enterprise technology and business priorities
• Lead the design, development, and lifecycle management of data products that support business operations, customer insights, and innovation
• Build and scale data science capabilities, including machine learning, AI-driven solutions, and advanced analytics to optimize retail operations and customer engagement
• Partner with business units, analytics, engineering, and product management to prioritize high-value use cases for both data products and data science applications
• Oversee data governance, stewardship, and metadata management to ensure accuracy, compliance, and trust
• Champion modern data and AI architectures, including cloud-native platforms, APIs, data streaming services, and real-time analytics
• Drive data democratization, ensuring business users can easily discover, access, and leverage insights
• Foster a product mindset and a research-to-production pipeline for data science solutions, ensuring scalability and usability
• Manage, mentor, and grow a high-performing team of data product managers, architects, engineers, and data scientists
• Define and monitor KPIs for both data products and data science initiatives, ensuring outcomes translate into tangible business impact
• Represent data strategy at the executive level, influencing enterprise decisions and advocating for innovation
• Additional tasks may be assigned

• 12+ years of experience in data leadership roles, including data product management, data platforms, and data science
• Proven track record of building and scaling data products and machine learning/AI solutions in large, complex organizations
• Strong expertise in modern data and AI/ML architectures, including cloud platforms (AWS, GCP, or Azure), data lakes/warehouses, and governance frameworks
• Exceptional executive communication and stakeholder management skills, with the ability to influence across business and technology functions
• Demonstrated success in leading cross-functional teams spanning data science, engineering, and product management