Senior Analytics Engineer, Product

PlayOn! Sports

Analytics Engineer building data models and products for high school sports platform. Collaborating with product and engineering teams to enhance data-driven decision making.

Posted 6/30/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Build experiences our customers love. Embed in the Video product org alongside PMs, designers, and engineers, using data to help shape and ship features athletes and their families actually use. You are a builder on the product team, not a reporting function next to it.
Strengthen and grow the data foundation. Make the models and pipelines we already have cleaner, more reliable, and more reusable, bring in new product, customer, and third-party signal we don't have today, and apply software discipline (version control, review, testing) throughout, so the team can trust the data and answer questions it currently can't.
Build experimentation into a system that scales. Stand up the data and analysis layer behind A/B tests so the team can design, run, and read experiments quickly and consistently, rather than rebuilding the plumbing each time.
Get instrumentation right at the source. Partner with product and engineering on event tracking and data contracts so the behavior we care about is captured accurately and completely from the start, then build the tests and monitoring that keep it that way.
Power product with live data APIs. Build and own data endpoints that feed real product experiences, from spec through production.
Surface insights and drive decisions. Turn the metrics that matter into clear, reliable insights and recommendations that move the business - building the dashboards and surfaces people trust to make decisions along the way.
Develop predictive and causal models. Develop predictive and causal models that move the business, from a pLTV model for revenue forecasting to a propensity model that targets discounts without cannibalizing revenue to causal-inference work that pinpoints which behaviors actually drive retention.

Requirements

What you’ll need

Strong SQL skills and comfort working with large analytical datasets: complex joins, window functions, and performance tuning on a cloud warehouse (we use Snowflake).
Strong data modeling instincts and a track record of clean, documented, reusable transformation layers (SQLMesh, dbt, or equivalent).
Production-grade Python for modeling, orchestration, and the analyses SQL alone won't cover, including the statistics to build an LTV or propensity model and reason honestly about causation versus correlation.
Experience building data products others depend on: APIs, pipelines, and dashboards, not just ad-hoc queries.
Stakeholder fluency: you translate business questions into metrics people agree on and influence decisions with what you build.
Familiarity with experimentation and A/B testing, enough to build the measurement and analysis that tests depend on.
Familiarity with AI-augmented development tools (Claude, Codex) as part of a modern workflow, and a bias for shipping where speed matters and perfect is the enemy of shipped.
**Bonus:** Background in data modeling for analytical or operational use cases; experience with real-time or streaming data systems; ML engineering basics like feature pipelines; sports, streaming, or B2C subscription experience.

Benefits

Comp & perks

Multiple medical insurance plans to choose from
Dental, vision life and disability insurance
Employee Emergency Fund
Company equity (stock options)
Open PTO policy
401K plan with company match
Hybrid/flexible work environment

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Hard Skills & Tools

SQLData ModelingPythonA/B TestingData TransformationPredictive ModelingCausal ModelingPerformance TuningData PipelinesDashboard Development

Soft Skills

Stakeholder CommunicationInfluencing DecisionsCollaboration