VP, Product Analytics

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.

What Success Looks Like

• Leadership operates from trusted, consistent product and business metrics.
• The Product Analytics team has clear priorities, strong technical standards, and consistently high-quality output.
• AI-enabled workflows materially improve analytical speed, quality, and capacity.
• Product teams use experimentation and evidence as standard parts of development.
• Amplitude instrumentation and taxonomy are reliable, governed, and useful.
• Major product launches have clear success criteria and rigorous post-release evaluation.
• High-impact analyses lead to concrete product, operational, and business decisions.

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.