Product Manager, Data Team

Description

Product Manager, Data Team

Location: Provo, UT

Type: Full-time

Angel is changing the future of American entertainment and is one of the fastest-growing Distributors in America. Our rapidly expanding library of light-amplifying stories has grown 10x in under 2 years. Gone is the old model where the deepest pockets pick the stories we share. Angel restores choice to our 3+ million guild members and growing who decide what we produce, what we take to theaters, and most importantly what parents bring to their homes. Check out angel.com/watch

Opportunity

Angel runs on decisions that data should be making faster: which stories to greenlight, where to focus attention with theatrical campaigns, what a Guild member sees first when they open the app, and whether the thing we shipped last week actually worked.

Today that data exists. It's spread across pipelines, dashboards, and the heads of many different people. We need a product manager who will turn it into a platform: one set of trusted data, one connected knowledge base, and high trust self-serve answers for every team that needs them.

This is a high-ownership role. You'll guide the roadmap for the data team, define what awesome looks like, and work closely with the data pipelines to refine them.

Why Join Angel?

• High-Growth Company: Angel is one of the fastest-growing media companies, with record-breaking independent theatrical releases and millions of streaming users worldwide. See our recent interview with Evan Shapiro
• Massive Impact: Your work will shape how audiences experience the stories they love—and help amplify light around the globe.
• Extreme Ownership: We are a team of owners and entrepreneurs. This is your chance to operate like a startup founder inside a fast-scaling media company.
• Mission-Driven Culture: We strive to amplify light in everything we do. Join a team that deeply cares about the impact of the stories we tell.
• Future of Streaming: Help build a streaming platform that competes with giants—without playing by their rules.

What You’ll Do

• Build the Angel knowledge platform. One place to ask a question and get a real answer, grounded in our own systems: application and analytics data, infrastructure and Datadog, build and deploy history, Notion, Slack, and the general institutional knowledge that currently lives in people's heads. Own ingestion, retrieval quality, freshness, and permissions.
• Build evaluation loops. Create feedback loops to track questions and improve answer trust and accuracy. Make it quick and easy to get trusted data.
• Own the data pipelines end to end. Ingestion, warehouse, transformation layer, semantic models, BI, and activation. Every layer has a customer and you're accountable for all of them.
• Increase platform trustworthiness. Data quality, freshness, lineage, and clear ownership of data sources. Answers that nobody trusts are worse than no answers at all.
• Make one number mean one thing. Define and govern the metrics the company runs on (retention, conversion, watch time, Pay It Forward rate, theatrical ROI) so finance, product, and content stop bringing three versions of the same answer to the same meeting.
• Turn data into slate decisions. Partner with content, theatrical, and marketing to build the reporting that informs what we greenlight, where we open, and how we spend.
• Treat internal teams as customers. Interview them, prioritize their requests, and measure whether they can correctly answer their own questions using AI
• Level up the experimentation. Drive forward the A/B testing platform and the practices around it. Keep it fast to launch a test, hard to read it wrong, and normal to kill a feature that didn't work.
• Set the guardrails. Identity, permissions that respect existing access, data boundaries, audit trails, and a clear policy on what agents may touch. Especially for member data.
• Prioritize ruthlessly. Say no to the projects that won’t move the needle and yes to the models that better build our foundation and best serve the team. Make build-versus-buy calls with real numbers attached.
• Measure it. Adoption by team, success rate, time saved, cost per outcome. Report the numbers honestly, including the ones that say a project isn't working.

What You’ll Need

• Product ownership over a technical domain. You think like an owner of a platform, not a ticket router for analytics requests.
• Technical depth. Fluent in AI, SQL, DBT. Fluent in a conversation about warehouse cost, event schemas, and pipeline design.
• Experimentation literacy. You understand power, sample size, guardrail metrics, and the ways an A/B test lies to you.
• Metric judgment. You can take a fuzzy business question and turn it into a definition that survives contact with finance.
• Storytelling. You can take a messy analysis and land it as a one-page recommendation an executive acts on.
• Stakeholder range. You work as comfortably with data engineers as with a theatrical distribution lead or a CFO.
• Prioritization under pressure. You can hold a roadmap steady while a dozen teams each believe their request is urgent.
• Adaptability and speed. Comfort working in a fast-moving environment with evolving priorities and incomplete information.
• Mentorship and team development. You elevate team members through feedback, coaching, and shared standards.

Required Experience

• 6-10+ years in data engineering, analytics, or data science
• 3+ years as a product manager for data, analytics, or platform products
• Strong SQL, used regularly and independently
• Hands-on experience with a modern data stack (cloud warehouse, transformation layer, BI or semantic layer)
• Proven track record owning an experimentation program end to end
• Experience defining company-level metrics and driving adoption of them
• Experience shipping internal platform products with real internal customers

Preferred Experience

• Streaming, media, or subscription business experience
• Familiarity with recommendation systems, personalization, or audience segmentation
• Experience with dbt, a semantic layer, and modern BI tooling
• Experience with a dedicated experimentation platform
• Customer data platforms, identity resolution, or attribution across web, mobile, and TV
• Working knowledge of privacy regulation (GDPR, CCPA) and data governance practice
• Exposure to ML or AI enablement, including feature stores and model evaluation
• Background in box office, theatrical distribution, or direct-to-consumer funnel analytics
• Familiarity with Angel, the Angel Guild, and our body of work (films, series, and other projects).