Fanatics Commerce is the global leader in licensed sports merchandise, operating a vertically integrated platform that designs, manufactures, and delivers officially licensed apparel, jerseys, headwear, and collectibles for major leagues, teams, and events worldwide. With more than 900 e-commerce sites and a global omnichannel presence across digital, in-venue, and retail, Fanatics Commerce reaches fans in over 180 countries and powers official fan experiences for many of the world's most iconic sports properties.
At Fanatics, we bring our BOLD Leadership Principles to life every day - building championship teams, obsessing over fans, acting with entrepreneurial speed, and delivering with a determined and relentless mindset.
ROLE OVERVIEW
The Manager of Data Engineering leads a team of Data Engineers and BI Engineers responsible for building and operating scalable, reliable data pipelines, platforms, and analytics products. This role owns engineering delivery, develops team members, and ensures high-quality, trusted data supports analytics, business operations, and data science. It also leads modernization across batch and streaming pipelines, data lakes, a governed semantic layer, and data governance foundations, while championing AI-first development. The Manager of Data Engineering delivers business and fan impact through BOLD leadership and execution excellence, leveraging data, automation, and AI-enabled insights.
HOW WILL YOU DRIVE IMPACT
Success is measured by the ability to deliver results through BOLD capabilities and measurable outcomes.
Team & Leadership Impact (Build Championship Teams)
Cross-Functional Leadership:
Partner with Data Science, Product, Engineering, Marketing teams, and Partners to support experimentation, marketing, funnel analysis, and scalable self-serve analytics
Translate business requirements into technical designs, data contracts, consistent metric definitions, milestones, and measurable outcomes
Communicate progress and risk clearly across technical and business stakeholders, making principled tradeoffs
People Leadership:
Build, manage, and grow a high-performing, inclusive team of Data Engineers and BI Engineers with clear expectations, ownership, and career development plans
Own the team's data strategy, roadmap, delivery commitments, and operational priorities
Manage team budget, resourcing, and tooling decisions while balancing new capabilities with platform reliability and technical debt
Fan & Customer Impact (Obsessed with Fans)
Deliver trusted, well-documented data products that give business partners consistent, governed answers for decision-making
Enable actionable insights and self-service analytics through enterprise BI platforms (MicroStrategy, Tableau, Omni, etc.) and AI tools
Optimize platform performance, scalability, and cost to withstand seasonal demand and traffic spikes tied to major sporting events and product launches
Apply data quality, lineage, privacy, and access controls so the data behind fan and customer experiences can be trusted
Innovation & Problem Solving (Limitless Entrepreneurial Spirit)
Champion AI-first development by embedding AI-assisted tools and agentic workflows into how the team designs, builds, tests, and operates data pipelines and BI solutions, with appropriate human review and validation
Build AI tools and data agents that let users get insights from data through natural-language interaction, alongside canned reports and dashboards
Build and maintain a semantic layer using tools such as dbt, Cube, and Snowflake that defines trusted business metrics once for BI tools and AI applications
Evaluate AI-related tooling based on measurable value, reliability, integration effort, and cost
Ownership & Execution (Determined & Relentless Mindset)
Own delivery of scalable, high-performance data pipelines and architectures on AWS (S3, EMR, Glue, Redshift, Lambda, Kinesis) and Snowflake-based cloud data warehousing
Lead batch and streaming ingestion with patterns…