We are seeking a visionary leader with expertise in data strategy, cloud architecture, and sports innovation to drive transformative solutions across the sports industry. This role blends strategic consulting with technical leadership, enabling organizations to leverage advanced data platforms, AI, and digital engagement to enhance athlete performance and fan experiences. 
 Strategic Leadership & Industry Expertise Define and execute enterprise-level data strategies aligned with sports business objectives and emerging technology trends. Guide sports organizations, technology providers, and brand partners in adopting modern solutions for competition systems, scheduling, and fan engagement. Communicate with executive stakeholders on the business value of modern data architectures and sports technology innovations. Technology & Architecture Architect cloud-based solutions (AWS, Azure, GCP) for real-time data ingestion, video streaming, and AI-powered game management ensuring high availability and low latency. Design and implement automated scoring, officiating, and game management systems using sensor data, real-time event detection, and camera technology. Formulate architectural trade-offs and deliver impactful strategic plans improving data integration, data quality, and interoperability across leagues and partners. Advanced Analytics & AI Develop and refine computer vision models for player and ball tracking, real-time officiating, and automated review systems. Implement agentic AI models that adapt dynamically to league rules, competition formats, and regulatory compliance. Drive predictive analytics for fan engagement, ticketing, merchandising, and personalized digital experiences. Data Ecosystem & Governance Establish frameworks for data sharing, API integrations, and interoperability across sports ecosystems. Ensure robust data governance, security, and compliance across platforms and partnerships. Technical Skills & Tools Cloud Platforms: AWS, Azure, GCP – including architecture design and optimization. Data Management: Data integration, data quality frameworks, and enterprise data delivery. Programming & Tools: Python, SQL, and experience with data engineering tools (Databricks, Snowflake). AI/ML: Computer vision, real-time analytics, and adaptive AI models. Architecture Practices: Designing scalable, secure, and high-performance solutions; diagramming and trade-off analysis. Modern Data Technologies: API frameworks, interoperability standards, and streaming architectures.