Senior Director, AI Data Architect

Major League Soccer has built Fan Genome, a 360° fan intelligence platform that unifies demographic, behavioral, transactional, and engagement data across the league. Fan Genome is evolving from a data platform into an AI-native intelligence foundation that enables semantic understanding, real-time fan context, and autonomous data-driven reasoning.

We are seeking a Senior Director, AI Data Architect to own the technical architecture and evolution of MLS’s next-generation data and intelligence platform. This is a deeply technical and hands-on leadership role focused on building the foundations for knowledge graphs, semantic layers, and context engineering that power AI systems, autonomous agents, and advanced fan intelligence at scale.

This role is oriented toward the next generation of data and intelligence platforms, emphasizing semantic understanding, contextual modeling, and AI-driven capabilities.

• Own the end-to-end architecture of MLS’s cloud-native Fan Genome platform, ensuring scalability, reliability, and extensibility for AI-native workloads.
• Lead the evolution from analytical data models toward semantic and graph-based representations of fan, content, commerce, and engagement domains.
• Define architectural patterns that support real-time, batch, and hybrid data processing for both human and machine consumption.

• Design and operate enterprise knowledge graphs modeling fan identity, relationships, behaviors, and interactions.
• Build and maintain a semantic layer that encodes business meaning, context, and logic for consistent use across systems and AI agents.
• Define ontologies, taxonomies, and metadata standards that support reasoning, inference, and explainability.
• Integrate graph systems with Lakehouse storage, APIs, and downstream AI platforms.

• Lead development of context engineering frameworks enabling AI models and autonomous agents to understand fan state, intent, and history.
• Enable agentic workflows for data exploration, insight generation, anomaly detection, and feature discovery.
• Partner with applied AI and ML teams to integrate feature stores, embeddings, vector search, and retrieval-augmented generation pipelines.
• Establish guardrails for AI-safe data access, grounding, and governance.

• Own and optimize real-time ingestion and event-driven processing using Apache Kafka, Amazon Kinesis, and Apache Flink.
• Manage distributed compute with Apache Spark for feature engineering, graph construction, and ML-adjacent workloads.
• Oversee open table formats such as Apache Hudi and Apache Iceberg to ensure ACID compliance, schema evolution, and incremental processing.
• Drive performance and cost optimization for low-latency analytical and AI workloads, including federated and zero-copy query patterns.

• Maintain and evolve data and intelligence APIs supporting batch access and low-latency per-fan queries.
• Advance identity and entity resolution to support unified fan profiles and graph-based reasoning.
• Ensure Fan Genome serves as the authoritative source of fan context for personalization, marketing, and AI-driven experiences.

• Establish enterprise-grade governance with frameworks such as AWS Lake Formation, including lineage, access control, and compliance.
• Define data and semantic contracts to ensure stability and trust for downstream systems and AI agents.
• Implement observability, data quality, and cost governance across data, graph, and AI pipelines.

• Build, mentor, and scale a high-performing team of data and platform engineers.
• Foster a culture of technical rigor, systems thinking, and experimentation.
• Act as a technical leader and partner to product, platform, and AI stakeholders across MLS.

• Bachelor’s degree in Computer Science or a related field required. Advanced degree preferred.
• 10+ years of experience in data engineering, platform engineering, or AI-adjacent systems.
• 8+ years of experience leading highly technical teams delivering production-grade platforms.