Senior Director, Data & Analytics Engineering-Fan Genome

Major League Soccer-posted about 2 months ago

$200,000 - $230,000/Yr

Full-time • Mid Level

Hybrid • New York, NY

1,001-5,000 employees

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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.

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

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
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, Looker, and optimizing query performance for high-concurrency workloads
Strong SQL, Python, and Scala
Infrastructure-as-Code (Terraform/CloudFormation), CI/CD, and container orchestration (Kubernetes)
High-level of commitment to a quality work product and organizational ethics, integrity and compliance
Ability to work effectively in a fast paced, team environment
Strong interpersonal skills and the ability to effectively communicate, both verbally and in writing
Demonstrated decision making and problem-solving skills
High attention to detail with the ability to multi-task and meet deadlines with minimal supervision
Proficiency in Word, Excel, PowerPoint and Outlook

Experience building customer or fan 360 platforms, identity resolution systems, and feature stores
Performance tuning for modern MPP OLAP systems and distributed query engines (e.g., Presto, Trino)
Strong background in self-service analytics strategies, data governance for BI, and cost optimization for analytical workloads
Knowledge of the Spanish Language (business proficiency)
Knowledge of the sport of soccer
Ability to travel and to work non-traditional hours, including evenings, weekends, and holidays

comprehensive medical, dental, and vision coverage
a $500 wellness reimbursement
generous Holiday and PTO schedule to promote work-life balance
career and professional development
on-the-job training
feedback
ongoing educational opportunities

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