Manager, Business Intelligence
About the role
Manager, Business Intelligence | Playlist Skip to main content A warning about recruiting scams Main navigation Our Culture Careers Press Contact Menu - Action View all jobs Manager, Business Intelligence Playlist | Finance | Full-Time Exempt (US) | United States Apply now
About the Company: At Playlist, life's richest moments happen when people step away from screens to move, connect, explore, and play. We're building the definitive platform for intentional living, connecting people with inspiring experiences in fitness, wellness, and beyond. With popular brands like Mindbody and ClassPass, Playlist empowers businesses and individuals, making it effortless for aspirations to become actions. Join us in reshaping technology's role to foster meaningful, real-world connections. Role You'll Play Manager, Business Intelligence (Product & Data Products)As Manager, Business Intelligence, you will lead how Playlist transforms complex product data into trusted, scalable insights—powering both internal decision-making and customer-facing analytics products. You’ll partner closely with Product, Product Analytics, and Engineering to define, build, and operationalize high-quality data models that support a highly customizable product ecosystem.Our stack: Snowflake + dbt Cloud powering Looker and Sigma Computing across internal and external analytics.This role sits at the intersection of product analytics, data modeling, and data product development. You’ll own internal product data foundations, enable fast and reliable insight generation, and lead the data layer powering both our external Analytics 2.0 product and internal AI-driven capabilities. You’ll manage and grow a small, high-impact team (2 direct reports with near-term hiring), setting a high bar for data quality, scalability, and business impact. Own Product Data Foundations Lead the development and maintenance of scalable, high-quality data models that represent our complex, customizable product ecosystem Transform raw, fragmented product data into clean, reliable datasets used across internal analytics and external products Establish and enforce best practices for data modeling, testing, and governance across product datasets Partner with Product & Product Analytics Work closely with Product Analytics to enable fast, accurate, and actionable insights on product performance, user behavior, and feature adoption Align on metric definitions, instrumentation needs, and data availability to ensure consistent and trusted reporting Support rapid iteration by balancing data quality with speed-to-insight Own External Data Products (Analytics 2.0) Own the data layer powering Analytics 2.0, ensuring datasets are accurate, performant, and intuitive for customer consumption Design and maintain customer-facing data models that balance flexibility with ease of use across a highly configurable product Partner with Product and Engineering to evolve Analytics 2.0 capabilities and expand reporting functionality Maintain a high bar for data quality, consistency, and trust in all external-facing datasets Build Internal AI Data Capabilities Partner with Product, Engineering, and Data teams to support the development of internal AI use cases (e.g., LLM-powered insights, automation, and decision support tools) Define and prepare high-quality, well-structured datasets that can be leveraged for AI/ML applications, including prompt-ready and feature-ready data Ensure data powering AI use cases is accurate, consistent, and aligned with business logic and product definitions Identify opportunities to leverage AI to improve internal analytics workflows, speed to insight, and data accessibility Help establish best practices for how BI contributes to AI development, including data preparation, validation, and ongoing monitoring Deliver Strategic Insights Lead deep-dive analyses on product performance and key business drivers, translating findings into clear recommendations Support product and leadership teams with data-informed decision-making and roadmap prioritization Build for Scale Collaborate with Data Platform and Engineering to improve data pipelines, automation, and overall system reliability Continuously improve workflows to support a growing data ecosystem and increasing product complexity Lead & Grow
