VP, Product Analytics

Crypto.com

. Set the vision, priorities, operating model, and quality standards for Product Analytics.

Posted 10/5/2026full-timeHong Kong • Hong KongLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading Product Analytics teams, utilizing advanced SQL and AI to enhance analytics operations, and establishing measurement frameworks for product success. Proficient in managing the Amplitude data stack and driving cross-functional collaboration to align analytics with business objectives.

Highest-signal resume keywords

Product Analytics LeadershipAdvanced SQL ProficiencyAI-Enabled Workflow DesignAmplitude Data Stack OwnershipExperimentation and Causal Inference Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Advanced SQLData ModelingMetric DefinitionExperiment DesignCausal InterpretationAnalytical Quality MeasurementData Pipeline DevelopmentValidation ControlsKnowledge Management SystemsDiagnostic Analysis

Soft Skills

People LeadershipExecutive CommunicationProduct JudgmentTeam DevelopmentDecision-Making

Tools & Technologies

AmplitudeDatabricksAI ToolsData Governance ToolsAnalytics Platforms

Industry Keywords

Consumer FintechMarket LiquidityTrading ExecutionTransaction-Heavy ProductsExperimentation Standards

Tech Stack

Tools & technologies

SQL

About the role

Key responsibilities & impact

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, guiding 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.
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 and improve delivery speed, quality, and consistency.
Establish governance, validation, and human review for high-stakes decisions.
Measure the system’s impact on turnaround time, analytical quality, experimentation throughput, and team capacity.
Establish trusted KPIs, source-of-truth metrics, dashboards, and executive business reviews.
Partner with Product, Engineering, Data, CRM, Growth, and other functions to align definitions, priorities, and business interpretation.
Establish experimentation 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.
Own the Amplitude data stack, including instrumentation strategy, event taxonomy, governance, data quality, and warehouse reporting integration.
Ensure new releases can be measured reliably and analytical models and pipelines remain traceable, reproducible, and trusted.
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 informing whether to iterate, scale, or stop.
Identify root causes, challenge weak hypotheses, and translate findings into recommendations and product actions.

Requirements

What you’ll need

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: consumer fintech, trading, marketplaces, or other transaction-heavy products.
Preferred: exchange mechanics, market liquidity, and multi-asset products.
Preferred: leading company-wide adoption of new analytics technologies and ways of working.