Job Location : Omaha,NE, USA
Position Title:Director of Data
Employee Status: Full Time
Location: Remote
Purpose:
Ideal candidates will be someone who sees data not just as a byproduct, but as a strategic engine. You'll have the opportunity to organize, package, and monetize the deepest dataset in the NIL space - and help define the future of athlete marketing.
Responsibilities:
Lead and execute a company-wide data strategyAudit our current systems, assets, and flows. Identify what's missing, what's valuable, and what's next. Develop a data architecture and strategy that supports scale, revenue, and AI-readiness.
Design and deliver AI-powered featuresBuild and deploy ML models that power recommendation engines, predictive analytics, audience segmentation, and more — improving athlete-brand matching and campaign performance.
Build scalable data pipelinesOwn the development of real-time and batch pipelines to integrate data from athlete profiles, social media, campaign performance, and third-party sources.
Develop robust, modern infrastructureEnsure we have a future-ready data platform that's fast, reliable, secure, and built for machine learning at scale.
Manage the full data lifecycleEnsure quality, consistency, availability, and governance of data from ingestion to model output — powering both internal decision-making and external features.
Optimize models in productionContinuously test, evaluate, and improve performance based on real-world usage and business impact.
Collaborate cross-functionallyPartner deeply with Product, Engineering, Marketing, and the Executive team to shape what we build and why — translating strategic questions into scalable solutions.
Grow and lead a world-class data teamHelp build, lead, and mentor a team that includes a data engineer, data scientist, and data manager — and scale it as the business grows.
Work Requirements, Experience, Education, and Skills:
5+ years in data engineering, data science, or technical leadership roles
Proven experience building cloud-native infrastructure and scalable pipelines
Deep knowledge of machine learning frameworks and deployment in production
A product mindset and ability to connect data to real business outcomes
Exceptional communication across both technical and executive audiences
Data Stack: Snowflake, SQL Server, Python
Visualization: Tableau
Cloud, AI, & ML: Azure AI services, TensorFlow, scikit-learn