Senior Director, Data & Analytics Engineering - Fan Genome Platform

Overview

Major League Soccer (MLS) has built Fan Genome, an advanced 360° fan intelligence platform that unifies demographic, behavioral, and transactional data to deliver hyper-personalization and real-time insights across every fan interaction. We are seeking a hands-on technical leader to own the architecture and evolution of MLS’s next-generation data platform—powering Fan Genome while delivering BI self-service and robust analytics engineering frameworks. This role brings together real-time streaming, distributed compute, open table formats, zero-copy analytics, and enterprise-grade governance to enable advanced analytics and fan engagement at scale.

Responsibilities

• Own the technical architecture and feature delivery of MLS’s next-generation cloud-native Lakehouse platform ensuring scalability, performance, and reliability
• Optimize and enhance existing real-time data pipelines built on Apache Kafka, Amazon Kinesis, and Apache Flink to maintain low-latency ingestion and event-driven processing at scale
• Manage and improve distributed compute workflows leveraging Apache Spark for large-scale batch processing, advanced feature engineering, and ML-adjacent workloads
• Oversee and refine open table format implementations (Apache Hudi, Apache Iceberg) to ensure ACID compliance, schema evolution, and efficient incremental processing
• Drive performance tuning and cost optimization for zero-copy analytics using modern distributed, MPP, column-oriented OLAP systems designed for real-time, high-concurrency analytical workloads (e.g., StarRocks) and query engines like Presto
• Maintain and extend robust data APIs for both batch exports and point (per-fan) queries, integrated with Fan Genome’s feature store
• Advance identity resolution capabilities to ensure accurate, unified fan profiles across multiple data sources
• Establish enterprise-grade governance and security with frameworks such as AWS Lake Formation for cataloging, lineage, and fine-grained access control
• Work with BI team to deliver BI self-service and analytics engineering frameworks, including:
  • Designing semantic models, data contracts, and governed data for consistency and trust in reporting
  • Building curated wide tables (OBTs) and optimized query layers for high-performance dashboards and ad-hoc analysis
  • Implementing data modeling best practices, version-controlled transformations, and automated testing to ensure reliability and scalability
• Build, mentor, and scale a world-class data and analytics engineering team, fostering a culture of technical excellence and innovation

Qualifications

• Bachelor’s degree in Computer Science or a related field required (Master’s preferred)
• 10+ years of progressive experience in data engineering or platform engineering, including 8+ years in leadership roles with a proven track record of delivering production-grade, large-scale data and analytics platforms

Required Skills

• Hands-on expertise in designing, deploying, and optimizing cloud-native data solutions on platforms such as AWS, Azure, or GCP
• Deep understanding of modern data architecture patterns, including Lakehouse design, data mesh principles, and data quality monitoring frameworks
• Demonstrated ability to translate complex business requirements into scalable technical solutions, collaborating with data management, security, and privacy teams to ensure compliance and governance
• Strong computer science fundamentals with proficiency in at least one advanced programming language (Python, Scala, or Java)
• Proven experience with distributed processing frameworks (e.g., Apache Spark, Apache Flink) and real-time streaming architectures
• Expertise in Lakehouse data platforms built on object storage and open table formats (e.g., Apache Hudi, Apache Iceberg) for ACID transactions, schema evolution, and incremental processing
• Proficiency in Infrastructure-as-Code, orchestration, transformation frameworks, containers, and observability tools
• Familiarity with data science and machine learning workflows, including feature engineering, model training pipelines, and integration with feature stores
• Deep BI and analytics expertise, including:
  • Designing and implementing analytics engineering frameworks for governed, reusable data models
  • Building semantic layers and curated wide tables (OBTs) to enable BI self-service at scale
  • Applying data modeling best practices, version-controlled transformations, and automated testing for analytics pipelines
  • Enabling advanced analytics and experimentation platforms for marketing, personalization, and revenue optimization
• Experience integrating with BI tools such as Tableau, Power BI, L