Product Analyst

Responsibilities

• 1) Product Measurement & Instrumentation
• Support and evolve the framework and systems that turn product behavior into trusted, actionable signals:
• Partner with Engineering to define and maintain event taxonomy, tracking standards, and instrumentation quality
• Build and maintain scalable, self-service dashboards for product teams
• Help establish and document clear metric definitions as a shared source of truth.
• Contribute to improving how and what we measure as the product evolves.
• Leverage AI to repeatedly unlock insights and recommendations from these signals
• Outcome: Product teams have reliable data to evaluate performance and make informed decisions.
• 2) Analytics & Data Foundations
• Strengthen the product analytics ecosystem and data models that power decision-making.
• Work within our product analytics stack (e.g., Mixpanel, Segment, Looker) to ensure accurate and accessible reporting.
• Build and maintain dbt models for core product entities and behaviors.
• Contribute to a clean, usable metric layer for business stakeholders.
• Help structure and analyze Voice of the Customer inputs (e.g., support, churn, NPS, sales feedback, customer interviews)
• Support data quality through testing, monitoring, documentation, and automation
• Deploy the best AI tooling available to make our data a differentiator
• Outcome: Product data is systematically consistent, accessible, and trusted.
• 3) Insight Generation & Reporting
• Translate product usage and customer signals into clear, actionable insights.
• Deliver recurring product analytics reporting (adoption, engagement, retention)
• Contribute to analytics readouts to business stakeholders and executives with clear findings and recommendations
• Perform analyses that connect product behavior to revenue and retention outcomes
• Support automated reporting and AI-assisted workflows while maintaining metric integrity
• Strive to make insights and reporting automated and self-service, heavily assisted through best practices with emerging AI techniques
• Outcome: Product and business stakeholders regularly use analytics and customer insights to inform roadmap and investment decisions.

Qualifications

• 5+ years of experience in product analytics or related roles. Experience working closely with digital product teams and influencing decisions through data.
• AI-First mentality. Must have a practical understanding of how AI is reshaping analytics workflows. The ideal candidate will have experience using AI to accelerate insight generation, documentation, and distribution while understanding how to build guardrails, trust, and safe operationalization of AI.
• Systems thinker & operator's mindset. Track record of building systems that scale across many groups.
• A “Builder” mindset . We have bold ambition, and we move fast. Whether we’re missing processes, systems, tools, data, or buy-in, your instinct must be to create, solve, and build paths that may not exist today.
• Comfort operating in ambiguity. Ability to prioritize, structure problems, and execute independently while collaborating across teams.
• Clear communicator. Experience presenting analysis to product managers and business stakeholders, translating complex findings into actionable recommendations. Strong analytical foundation. Advanced SQL required. Experience with event-based analytics and familiarity with tools such as Mixpanel (or similar), Looker (or similar BI tools), Segment (or similar), and dbt. Domain familiarity (preferred). Exposure to public cloud environments (AWS, GCP, Azure), FinOps/cloud cost management, and enterprise SaaS company concepts is preferred.

How success will be measured

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