About the Job
The Director of Data Engineering, Platform & Governance will architect, build, and operate the Los Angeles Clippers' next-generation data platform powering fan engagement, business operations, and AI innovation across the Intuit Dome ecosystem.
This role is responsible for designing a secure, real-time, analytics- and AI-ready and unified data platform that transforms fan interaction data-including mobile app events, ticketing, marketing, concessions, computer vision, and biometric signals-into trusted, cataloged, governed datasets that power marketing, sales, operations, and AI applications.
Reporting to the Chief Data, Analytics & AI Officer, this role combines hands-on data platform leadership with enterprise data and AI governance responsibilities. During the initial phase, the Director will work with a small engineering team and will be expected to contribute directly to platform architecture, pipeline development, and governance implementation.
This is a full-time role based in Inglewood, CA and is eligible for our competitive benefit offering including medical, dental, vision, 401(k) plan with company contribution, Well-Being Allowance, and more. Due to the nature of the sports and live event industry, the candidate must be available to work a flexible schedule including evenings, weekends, and holidays, particularly around Clipper's home games and other high-profile events.
What you'll do:
Data Platform Architecture & Engineering; Define and deliver the Clippers' modern data and AI platform enabling real-time fan intelligence and AI.
- Architect and implement the end-to-end data platform, including edge ingestion, streaming pipelines, batch processing, and curated analytics and MLOps layers in collaboration with the infrastructure team
- Build and maintain scalable, low-latency data pipelines integrating ticketing, CRM, MarTech, concessions, IoT, and fan experience systems
- Design analytics-ready data models and marketing schemas that power segmentation, campaign orchestration, and revenue optimization
- Develop real-time data services and APIs supporting fan engagement and intelligence applications and AI products
- Establish engineering standards for data quality, observability, reliability, governance, security, and cost optimization in alignment with existing infrastructure and cyber standards and policies
- Ensure the platform supports advanced analytics, machine learning, and AI product development in collaboration with the infrastructure team
Data Engineering Leadership; Lead the design and execution of the organization's data engineering strategy.
- Define and execute the data platform roadmap aligned with fan intelligence and AI initiatives
- Provide technical leadership across data engineering and MLOps environments
- Partner closely with AI, analytics, marketing, sales, product, security, and technology teams
- Influence vendor selection, tooling strategy, and platform architecture decisions
- Drive performance, scalability, and cost optimization across the data ecosystem
- Act as a hands-on technical leader, contributing to architecture design and engineering implementation
Data Governance & Data Trust; Establish enterprise-grade governance ensuring data is secure, trusted, and usable at scale.
- Define and enforce data governance policies and standards including quality, privacy, security, and retention
- Establish data ownership, stewardship, and enterprise data definitions
- Implement frameworks for data quality monitoring, lineage tracking, and metadata management
- Define controls for data access, sharing, and usage across internal teams and external partners in collaboration with the infrastructure team
- Partner with Legal and Security to ensure compliance with CCPA, GDPR, emerging AI regulations, and internal policies
AI Governance & Responsible AI; Establish the governance frameworks ensuring AI systems are safe, compliant, and trustworthy.
- Operational lead for the AI Governance Council, establishing governance frameworks and decision processes. Classify AI systems by risk level and regulatory impact and define approved vs restricted use cases
- Define standards for AI model lifecycle governance, including approval, monitoring, and auditability
- Implement controls for model monitoring, drift detection, bias mitigation, and human-in-the-loop oversight
- Establish guidelines for AI explainability, transparency, and customer impact review
- Ensure AI models are trained and deployed using approved, high-quality data sources and meet governance standards
Your Background, Skills and Qualifications
- 9+ years of experience in data engineering, data platform architecture, or data infrastructure
- Proven experience designing and operating modern data platforms at scale
- Strong experience with real-time streaming, batch processing, data lakehouse architectures and analytic data business logic
- Hands-on experience building data pipelines and distributed data s