Manager Product Analytics
Pinwheel
Description
Pinwheel - Building the future of financial services in partnership with the biggest brands
"I love my banking app!" said no one, ever.
Pinwheel is on a mission to change that. We believe banks and financial service providers represent the greatest opportunity to build 10x better experiences, especially in the AI age. We're building the next generation of financial products alongside some of the biggest names in the market including Robinhood, DoorDash, Credit Karma, American Express, Discover, Intuit, Acorns, Visa and more.
If you're excited by the idea of having your work touch and impact the lives of hundreds of millions of consumers, Pinwheel is the place for you.
If you get stoked about building products alongside the biggest brands in the world, Pinwheel is the place for you.
If you want to join a scrappy, hustling team that is obsessed with defining the future of financial services, Pinwheel is the place for you!
Pinwheel has raised $77M from top-tier investors such as Coatue, Notable, First Round, Upfront, Primary, American Express, Franklin Templeton, Indeed, Semper Virens and more.
Who are we looking for?
Pinwheel is looking for an Analytics Manager who will serve as a strategic partner across Product, Engineering, Design (EPD), and Go-to-Market (GTM) teams. This role goes beyond dashboardsâit's about proactively driving insights, surfacing high-impact opportunities, and shaping product and business strategy.
You'll help Pinwheel understand not just what is happening in our products, but why, by combining quantitative and qualitative signals, and business context to guide decisions across the organization. You'll play a critical role in influencing roadmap priorities, shaping customer best practices, and empow
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About Pinwheel
Pinwheel is a fintech company that provides income verification and payroll connectivity solutions. It helps businesses and financial institutions to access payroll data and verify income.
Job Stats
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Interview Prep Guide
Preparation Strategy
Finally, be sure to review the job description and requirements carefully, and be prepared to discuss how your skills and experience align with the role. Practice answering questions about your experience, and be prepared to provide specific examples of your accomplishments.
Likely Interview Rounds
- 1. Screening call~30 min
What to prep: Review your experience in product analytics, and be prepared to discuss how you've driven insights and shaped product strategy in previous roles. Research Pinwheel's mission and values to understand their approach to financial services.
- What experience do you have in product analytics?
- How do you stay up-to-date with industry trends in financial services?
- Can you walk me through a time when you had to drive insights and shape product strategy?
- 2. Technical~60 min
What to prep: Review data analysis and visualization tools, and be prepared to discuss your approach to data analysis. Practice explaining complex data concepts in simple terms.
- How do you approach data analysis and visualization?
- What tools do you use for data analysis, and why?
- Can you explain a complex data concept to a non-technical audience?
- 3. Behavioral~60 min
What to prep: Review your experience in communicating complex data insights, and be prepared to discuss how you've influenced roadmap priorities with data-driven insights. Practice answering behavioral questions using the STAR method.
- Tell me about a time when you had to communicate complex data insights to a non-technical audience.
- Can you describe a situation where you had to influence roadmap priorities with data-driven insights?
- How do you handle conflicting priorities and tight deadlines in a fast-paced environment?
Most Likely Questions
- What do you know about Pinwheel's mission and values?
- How do you stay current with industry trends in financial services?
- Can you walk me through your process for analyzing complex data sets?
- How do you handle conflicting priorities and tight deadlines?
- Can you describe a situation where you had to drive insights and shape product strategy?
Common Pitfalls
- Lack of preparation for behavioral questions
- Inability to explain complex data concepts in simple terms
- Limited knowledge of industry trends in financial services
Free Prep Resources
- • LeetCode
- • DataCamp
- • Coursera - Data Science Specialization