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.