VP, Product Analytics

About the role

In this role, you will spearhead the Product Analytics team, setting the vision and quality standards while developing a high-performing team. You will be responsible for establishing trusted metrics and dashboards, ensuring that analytics directly inform business decisions.

You will also build an AI-native analytics operating system that automates workflows and enhances the quality of insights. Success in this position will mean transforming ambiguous questions into actionable analyses and fostering a culture of experimentation within product development.

The salary range for this position is between $130,000 and $200,000, which reflects the seniority and responsibilities of the role.

Skills that matter here

• AI: This role involves using AI to redesign analytics operations and create automated workflows.
• SQL: Advanced SQL skills are crucial for reviewing data-engineering processes and ensuring data quality.
• data platforms: Experience with modern data platforms is necessary for managing the analytics stack.
• Amplitude: Ownership of the Amplitude data stack is a key responsibility in this position.
• analytics: The role focuses on driving high-impact analysis and establishing trusted KPIs.

Who this role suits

• A candidate who thrives in fast-paced environments and enjoys leading teams.
• Someone with a strong technical background and hands-on experience in analytics.
• An individual who is comfortable transforming complex data into clear business insights.
• A leader who values experimentation and evidence-based decision-making.

From the employer

Lead Product Analytics

• Set the vision, priorities, operating model, and quality standards for Product Analytics.
• Hire, coach, and develop a high-performing team.
• Review analytical and data-engineering PRs, providing guidance on SQL, data models, pipelines, metric definitions, and methodology.
• Represent Product Analytics in executive and product decision-making.
• Allocate team capacity toward the company’s highest-impact opportunities.

Build an AI-native analytics operating system

• Design how analytics work moves from business questions to trusted decisions across intake, data discovery, analysis, validation, reporting, and knowledge management.
• Build reusable AI tools to automate repetitive workflows, encode analytical standards, and improve the speed, quality, and consistency of delivery.
• Establish appropriate governance, validation, and human review for high-stakes decisions.
• Measure the system’s impact on turnaround time, analytical quality, experimentation throughput, and team capacity.

Own product and business reporting

• Establish trusted KPIs, source-of-truth metrics, dashboards, and executive business reviews.
• Ensure reporting is accurate, consistent, and focused on decisions—not simply monitoring performance.
• Partner with Product, Engineering, Data, CRM, Growth, and other functions to align definitions, priorities, and business interpretation.

Build an experimentation culture

• Make experimentation and evidence core parts of product development.
• Establish standards for hypotheses, success metrics, guardrails, experiment design, causal interpretation, and rollout decisions.
• Use AI and automation to streamline experiment intake, validation, analysis, and readouts while maintaining analytical rigor.
• Help product teams move from opinion-led decisions to repeatable test-and-learn practices.

Own analytics platforms and data quality

• Own the Amplitude data stack, including instrumentation strategy, event taxonomy, governance, data quality, and integration with warehouse reporting.
• Set standards for product instrumentation and ensure new releases can be measured reliably.
• Set standards for and review analytical models and pipelines, ensuring metrics remain traceable, reproducible, and trusted as products evolve.

Drive high-impact analysis

• Lead diagnostic deep-dives into activation, conversion, retention, user behavior, market liquidity, trading execution performance, and product health.
• Define measurement frameworks and success criteria for major product launches.
• Oversee post-release evaluations that inform whether the company should iterate, scale, or stop.
• Identify root causes, challenge weak hypotheses, and translate complex findings into clear recommendations and product actions.

Qualifications

• Proven experience leading Product Analytics teams in a complex, fast-moving organization.
• Strong hands-on technical judgment, advanced SQL, and experience with modern data platforms such as Databricks.
• Demonstrated experience using AI to redesign analytics operations—not merely improve individual productivity.
• Ability to design and implement AI-enabled workflows, reusable agents or tools, validation controls, and analytics knowledge systems.
• Experience owning a product analytics platform; deep Amplitude experience is strongly preferred.
• Strong knowledge of experimentation, causal inference, product measurement, and diagnostic analysis.
• Ability to turn ambiguous business questions into rigorous analysis and clear decisions.
• Strong product judgment, people leadership, and executive communication skills.

Preferred Experience

• Consumer fintech, trading, marketplaces, or other transaction-heavy products.
• Exchange mechanics, market liquidity, and experience with multi-asset products.
• Leading company-wide adoption of new analytics technologies and ways of working.

Benefits

• Competitive salary
• Attractive annual leave entitlement including: birthday, work anniversary
• 401(k) plan with employer match
• Eligible for company-sponsored group health, dental, vision, and life/disability insurance
• Work Flexibility Adoption. Flexi-work hour and hybrid set-up
• Aspire career alternatives through us. Our internal mobility program can offer employees a diverse scope.