Senior Analytics Engineer, Product

About the role

PlayOn is the largest platform for high school sports in the US, reaching millions of fans, parents, coaches, and student athletes across NFHS Network, MaxPreps, and GoFan. This role sits on the Video product team, reporting to the Senior Director of Product, and is the data engine behind it: the person who turns raw event, viewership, and subscription data into the models, metrics, and data products the rest of the team builds on. You will partner closely with the central Data Platform team to adopt and uphold their standards while constructing transformation layers and models that the platform can reuse for broader organizational impact. This is a builder-focused position designed for someone who thrives on turning complex datasets into systems that drive better decisions and create scalable data products. You will balance technical rigor with speed, navigating ambiguity to build foundational assets that elevate the entire Video product organization.

Key facts

What you'll do

Establish a trusted product data foundation by building reusable data models, transformation layers, and pipelines that serve as the single source of truth for the Video product team while strictly adhering to shared Data Platform standards. Design and implement a scalable experimentation framework that enables teams to run, measure, and iterate on A/B tests consistently and efficiently across the organization. Develop production-ready data products such as APIs, dashboards, and analytical tools that power customer-facing experiences and provide stakeholders with trusted metrics for strategic decision making. Own the reliability of product instrumentation by ensuring event tracking, data contracts, testing, and monitoring are in place so product behavior is captured accurately and remains trustworthy over time. Create predictive insights through the development of predictive and causal models that improve forecasting, retention, pricing, and overall product strategy for the Video unit. Collaborate intensively with product managers, designers, and engineers within the Video product org to embed data thinking directly into the feature development lifecycle. Translate ambiguous business problems into structured analytical approaches, defining clear metrics and success criteria while maintaining a balance between speed and technical rigor. Codify business logic into reusable transformation layers and models so that the logic you build gets adopted and reused beyond your immediate team. Act as the primary liaison between the product team and the central Data Platform team, ensuring alignment with platform standards and driving broader adoption of your data products. Continuously evaluate new product, customer, and third-party signals to expand the data landscape and uncover fresh opportunities for insight and optimization.

Requirements

Candidates must possess a Bachelor's degree or higher in a quantitative field such as Computer Science, Engineering, Mathematics, or a related discipline. You must have demonstrated professional experience as an Analytics Engineer or Data Engineer, with a proven track record of building and maintaining data pipelines and transformation layers in a production environment. Strong proficiency in SQL is required, along with hands-on experience using modern data modeling tools and frameworks to develop scalable and maintainable data solutions. You should be comfortable working with complex, multi-source datasets and transforming them into coherent, reliable data products that non-technical stakeholders can easily consume. Experience with data testing, monitoring, and observability practices is essential to ensure data quality and reliability across the full lifecycle of data products. Excellent communication skills are mandatory, as you will regularly translate technical concepts into clear narratives for product managers and cross-functional stakeholders. A mindset oriented toward building reusable platforms and standardized processes is critical, as you will be responsible for shaping data products that other teams can adopt and extend. Self-driven ownership and the ability to navigate ambiguity while delivering concrete, production-grade data outcomes are fundamental to success in this role.

Nice to have

Experience with modern data stack tools such as dbt, Snowflake, BigQuery, or similar cloud data platforms. Familiarity with Python or additional programming languages for data manipulation and tooling. Background in experimentation platforms and methods for measuring causal impact. Understanding of data governance, lineage, and documentation best practices. Experience working with high-volume event data in sports, media, or entertainment domains.

Practical notes

This is a remote role. No official apply page is referenced in the source material.