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.