Major League Soccer has built Fan Genome, a 360° fan intelligence platform unifying demographic, behavioral, transactional, and engagement data across the league. MLS operates a modern cloud-native Lakehouse architecture built on medallion design, decoupled storage and compute, and distributed processing at scale.

We are seeking a Director of Analytics Engineering & BI Platform to lead the analytics engineering function and fully enable the BI organization on top of this existing foundation. This role focuses on curating scalable data products, standardizing enterprise metrics, strengthening semantic governance, and ensuring BI and AI use cases are supported by trusted, high-performance datasets.

Lakehouse Enablement and Analytics Integration

• Leverage MLS’s existing Lakehouse architecture across bronze, silver, and gold layers to deliver scalable analytics and BI-ready data products.
• Define modeling, transformation, and serving standards that align analytics engineering workflows with the underlying platform.
• Partner with data engineering to ensure ingestion pipelines and core datasets support downstream BI and semantic requirements.
• Establish best practices for incremental processing, schema evolution, and open table usage within analytics use cases.
• Structure analytics workloads to take full advantage of decoupled storage and distributed compute capabilities.

Analytics Engineering & Domain Data Products

• Lead development and refinement of curated gold-layer datasets aligned to fan, commerce, content, and engagement domains.
• Implement scalable transformation frameworks leveraging distributed compute engines such as Spark or equivalent.
• Design domain-oriented data products built on large, incrementally processed tables rather than fragmented reporting schemas.
• Standardize transformation logic, automated testing, version control, CI/CD, and lineage tracking.
• Ensure data products are reusable, well-documented, and trusted across the organization.

BI Curation & Enterprise Enablement

• Curate and optimize analytics datasets specifically for Power BI and other enterprise BI tools.
• Design scalable BI-ready datasets that balance usability, governance, and performance.
• Optimize semantic models for query performance, refresh efficiency, and executive reporting requirements.
• Establish standards for dataset certification, lifecycle management, and metric consistency.
• Define best practices for incremental refresh, aggregation strategies, and performance tuning within Power BI.
• Provide structured partnership and ongoing support to ensure the BI team is fully enabled with stable, performant, and governed datasets.

Semantic Governance & Knowledge Graph

• Operate and evolve a centralized semantic layer that standardizes KPI definitions, hierarchies, and metric logic.
• Encode business rules programmatically to eliminate duplication across dashboards and downstream systems.
• Maintain a pragmatic semantic knowledge graph capability to model key entities and relationships across fan, commerce, and engagement domains.
• Ensure semantic assets are machine-readable and consistent across BI, APIs, and AI use cases.
• Implement metric certification, versioning, and change management processes.

Data Quality, Observability & Contracts

• Implement and mature a comprehensive data quality framework across medallion layers.
• Define SLAs for critical datasets and enterprise KPIs.
• Establish automated validation, reconciliation testing, anomaly detection, and freshness monitoring.
• Embed quality controls and semantic validation into analytics workflows.
• Formalize data and semantic contracts between upstream producers and BI consumers.

AI-Ready Data & Metadata Foundations

• Translate business logic and KPI definitions into structured, machine-readable representations.
• Maintain metadata-driven documentation capturing metric logic, lineage, and transformation dependencies.
• Ensure BI datasets and semantic assets are prepared for AI and advanced analytics use cases.
• Support integration with feature stores and emerging AI workflows where appropriate.

BI & Asset Migration

• Lead structured migration of legacy reporting assets and fragmented BI datasets into the unified Lakehouse and semantic architecture.
• Define migration playbooks including inventory, reconciliation, validation, parallel runs, and deprecation planning.
• Ensure metric parity and stakeholder confidence during transitions.

Leadership, Team Management & Stakeholder Partnership

• Lead, manage, and develop a high-performing analytics engineering team, including hiring, coaching, and performance management.
• Establish clear goals, operating rhythms, and accountability frameworks.
• Create career paths aligned to modern analytics engineering practices.
• Serve as a data champion across the organization, promoting enterprise data standards and metric consistency.
• Establish clear operating models across data engineering, analytics engineering, BI, and AI teams.
• Partner with executive stakeholders to translate business KPIs into governed semantic models and scalable data products.
• Foster a culture centered on engineering rigor, data quality, collaboration, and stakeholder alignment.

Education and Experience

• Bachelor’s degree in Computer Science, Engineering, or related field required. Advanced degree preferred.
• 10+ years of experience working with modern cloud-native data platforms and enterprise BI ecosystems.
• 8+ years leading engineering or analytics teams in distributed data environments.

Required skills

Modern Lakehouse & Distributed Systems

• Proven experience working within large-scale Lakehouse architectures with medallion modeling.
• Strong understanding of decoupled storage and compute architectures.
• Experience with open table formats such as Apache Iceberg or Apache Hudi.
• Expertise in distributed processing engines such as Apache Spark.
• Experience designing analytics workflows over large tables in object storage environments.

BI Curation & Power BI Expertise

• Strong experience designing, optimizing, and governing Power BI datasets at scale.
• Deep understanding of semantic modeling within Power BI, including measures, calculation groups, incremental refresh, and performance tuning.
• Experience building certified enterprise datasets supporting executive reporting and self-service analytics.
• Ability to align Lakehouse data products with BI consumption-layer requirements.

Analytics Engineering & Semantic Modeling

• Experience designing domain-oriented data products in Lakehouse environments using medallion layering and distributed transformations.
• Demonstrated experience building governed semantic layers and enterprise metrics frameworks.
• Ability to encode business rules programmatically and maintain machine-readable metric logic.
• Working knowledge of semantic knowledge graph concepts and entity relationship modeling.

Data Quality & Observability

• Experience implementing automated data validation frameworks in distributed architectures.
• Strong understanding of reconciliation testing, SLA enforcement, and lineage tracking at scale.

AI-Ready Data Systems

• Familiarity with feature stores, embeddings, and vector indexing concepts.
• Experience maintaining metadata-driven systems that support machine-readable business semantics and AI consumption of analytics assets.

Total Rewards

Major League Soccer offers a competitive starting base salary of $200,000 - $230,000.00, based on individual qualifications, market financials, and operational business needs. We are committed to providing a Total Rewards package that attracts, supports, engages, and retains talent. Our benefits package includes comprehensive medical, dental, and vision coverage, a $500 wellness reimbursement, and generous Holiday and PTO schedule to promote work-life balance. We also prioritize career and professional development, offering on-the-job training, feedback, and ongoing educational opportunities.

We believe in the power of in-person collaboration to fuel creativity, strengthen connections, and cultivate a vibrant workplace. As a result, employees are required to work from an MLS office at least four days a week. We understand the value of balance, so employees also have the flexibility of working remotely on Fridays, along with the option to take up to two additional remote flex days each month.

At Major League Soccer, we are proud to be an equal opportunity employer. We value diversity and inclusion and believe that a diverse workforce enhances our ability to compete in the marketplace. We are committed to providing equal employment opportunities to all individuals regardless of race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law.

We are dedicated to ensuring that individuals with disabilities are provided reasonable accommodation throughout the job application or interview process, essential job functions, and other benefits and privileges of employment. If you require accommodation, please contact us to request it.

Join our team and be part of the Major League Soccer family, where we elevate the game and inspire greatness!