Vice President, Data Systems

1 day ago

El Segundo, CA, United States The National Basketball Association Full-time
Job Title: Vice President, Data Systems

Department: Basketball Analytics

Reports to: Assistant General Manager, Strategy & Data Systems

Position Summary: The Vice President, Data Systems will provide strategic and technical leadership for the organization's data, software engineering, and technology infrastructure, overseeing the design, development, and continuous improvement of enterprise data platforms, applications, and systems. This role is responsible for establishing the technical vision, architecture, and engineering standards that enable the seamless collection, integration, management, and delivery of data across basketball operations, analytics, performance, medical, and business functions. The Vice President will drive technology strategy, data governance, system reliability, and product innovation while ensuring solutions are secure, scalable, and aligned with organizational priorities. Additionally, this role will lead and develop a multidisciplinary engineering and product team, fostering a culture of innovation, collaboration, operational excellence, and continuous improvement.

Essential Functions (Duties & Responsibilities):
  • Technical Leadership for the Organization: Set the technical vision and engineering standards for Data Systems across data engineering, web and mobile development, and platform infrastructure. Make sure each system and the overall architecture is reliable, scalable and secure, and that it optimally serves coaches, front office, scouts, medical and performance staff, and players.
  • Data Platform Architecture: Own the end-to-end architecture of the organization's cloud data platform, including ingestion, transformation, storage and serving layers. This covers NBA and non-NBA data, player tracking and motion capture, scouting and qualitative data, and health, performance, and wearables data. Support and direct the ongoing move to modern, production-grade infrastructure (e.g. medallion architecture, orchestrated pipelines, lineage tracking) so analysts and modelers get curated, validated, and development-ready datasets.
  • Product and Application Development: Lead the design, development and delivery of internal web and mobile applications that optimally funnel data and model outputs to decision makers. Oversee product management across the Lakers enterprise, turning user needs into prioritized roadmaps, intuitive interfaces and production-caliber releases, with attention to usability, accessibility, offline and travel use, and adoption across departments.
  • Inter-Department Integration: Make sure Data Systems engineering serves the collective needs of the Performance Strategy, Analytics Strategy, and Cap Strategy divisions. Build shared data models, APIs, and service layers so information moves from collection to analysis to application, without duplicated effort. Establishing consistent and reliable sources of truth is critical.
  • Engineering Excellence and Operations: Set and enforce software and data engineering best practices: version control, code review, CI/CD, infrastructure-as-code, automated testing, observability and incident response. Treat data, ML, and application infrastructure as production software, with clear service-level expectations for uptime, latency and data freshness, especially for time-sensitive periods including gameday, draft, and trade deadline.
  • Data Governance, Security, and Quality Control: Set organizational standards for data access, permission management, encryption, data retention, and audit trails. These standards make sure sensitive athlete, medical and transaction data complies with NBA, CBA and organizational security and privacy requirements. Oversee validation frameworks, schema enforcement, and automated quality monitoring.
  • Technology Planning: Own the long-term roadmap for data and software infrastructure. That includes evaluating build-versus-buy decisions, data vendors and feed providers, cloud storage and compute strategy, and emerging tools such as vector databases, ML platforms and AI-assisted development. Manage the division's technology budget, vendor contracts and cloud spend to ensure interoperability, security, and return on investment with an eye to emerging technologies.
  • Staff Management: Recruit, develop and manage a multidisciplinary team of data engineers, developers and full-stack engineers, product and design staff, and platform/DevOps engineers. Set goals, define career ladders and technical growth paths, mentor staff, and build a culture of ownership, documentation and close collaboration with analysts and end users.
Responsibilities subject to change based on organizational n