Product Analytics Manager

Stocktwits

6h•Remote

About The Position

We are looking for a sharp, curious, and strategic Product Analytics Manager to bridge the gap between complex data and product evolution. In this role, you will act as a key partner to our Product team. You will be responsible for turning raw user behavior data into a narrative that shapes our product roadmap and improves the overall user journey. As a manager, you will not only perform deep-dive analyses but also establish the frameworks and metrics that define success for our organization.

Requirements

Minimum of 5 years in data analytics, with at least 2 years specifically focused on product analytics or a similar growth-focused role.
Advanced SQL skills are a requirement. Experience with Python for statistical modeling is highly preferred.
Deep familiarity with product analytics platforms like Mixpanel (preferred), Amplitude, or Heap.
Proven ability to build intuitive dashboards using tools such as Looker (preferred), Tableau, or Sigma.
A track record of moving beyond "what happened" to explaining "why it happened" and "what we should do next."
Experience mentoring junior analysts or leading cross-functional projects in a fast-paced environment.
A User Advocate: You are obsessed with understanding user intent and behavior.
A Clear Communicator: You can explain p-values and confidence intervals to a broad audience without losing the room.
A Problem Solver: You don't just wait for a ticket; you look at the data, find the leak in the funnel, and propose a solution.
Detail Oriented: You understand that data integrity is the foundation of trust and you have a low tolerance for "dirty" data.

Responsibilities

Strategic Partnership: Collaborate directly with Product Managers and Designers to identify opportunities for product improvement and growth.
User Journey Mapping: Analyze the end-to-end user lifecycle to identify friction points, drop-off signals, and high-value behaviors.
A/B Testing and Experimentation: Design, execute, and interpret experiments to validate new features and product pivots.
Data Infrastructure: Partner with Data Engineering to ensure our tracking and telemetry are accurate, scalable, and meaningful.
Insight Synthesis: Translate complex datasets into clear, actionable insights for non-technical stakeholders through visualization and storytelling.
Metric Ownership: Define and monitor Key Performance Indicators (KPIs) such as retention, conversion rates, and lifetime value.