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.