Product Experience Analytics Manager
Harnessing insights from data is central to delivering a best-in-class customer experience.
This role combines analytics expertise with a deep understanding of sportsbook experience. You'll work closely with stakeholders to shape the development and strategic direction of our product.
Key Responsibilities:
* Partner with business stakeholders and product development teams to provide actionable insights, reports, and research.
* Lead analytics for A/B testing capabilities, working with UX, Data Engineering, and Frontend Engineering teams.
* Define requirements for data collection infrastructure, ensuring high-quality implementation.
* Support the evolution of our analytics stack and contribute to developing our self-service data platform.
* Recruit, manage, and upskill a high-performing analytics team.
* Promote a product-led mindset and advocate for analytics in enabling our sportsbook as a product-centric organization.
* Drive adoption of analytics and reporting as part of the Definition of Done for product features.
* Map customer journeys and provide insights to UX and CX teams to inform design decisions.
Qualifications:
* Strong domain knowledge in B2C sports betting and understanding of sportsbook user experience.
* Proven experience leading and developing analytics teams, mentoring analysts, and fostering collaborative cultures.
* Deep expertise in digital data collection and modeling, designing tracking strategies aligned with product and business needs.
* Proficiency in SQL, DBT, Redshift, S3, Python, and familiarity with Power BI or Amazon QuickSight.
* Hands-on experience with event data pipelines, such as Snowplow, for capturing granular user interactions.
* Experience with A/B testing frameworks like GrowthBook, understanding experimental design and statistical analysis.
* Familiarity with analytics tools like Adobe Analytics or Google Analytics; helpful for shaping in-house analytics development.
* Experience with cloud data platforms (AWS, Azure, GCP), including data storage, processing, and access controls.
* Strong collaboration skills with Data Engineering teams to define requirements and ensure data quality.
* Solid understanding of statistical concepts, hypothesis testing, and confidence intervals.
* Knowledge of A/B testing methodologies, segmentation, control logic, and post-test analysis.
* A growth mindset, committed to continuous learning and coaching your team to reach their full potential.