We are looking for a self-motivated Product Analyst with a strong technical backbone and a strong sense of ownership. As a key member of the Product Analytics team, you will build the data tools the company runs on - data models, AI-assisted analytics, Claude skills and monitoring - and make sure every team can use data accurately and independently.

You will work closely with Product, R&D, Customer Success, Finance and other cross-functional teams, balancing hands-on data engineering with analysis. Chargeback domain knowledge is a plus, not a prerequisite - we'll teach you the domain; you bring the technical depth.

What matters most to us

1. You deliver end-to-end, on your own. Give you a loose problem, and you take it from question to working result. You explore the directions, decide which ones matter, ship, then improve. We don’t expect perfection. We expect you to keep moving without hand-holding.
2. You make the complex simple. Give you a messy problem, and you’ll dig into it, reduce it to its essence, and build the simplest solution that solves it. No extra layers, no extra metrics, so the people you build for get something they understand and can use right away.

Responsibilities

• Build and own AI-assisted data tools: Claude skills, self-serve analytics, and agent-based workflows that let teams query, analyze, and act on data accurately without going through an analyst.
• Design and maintain the data layer behind these tools - Snowflake data models, dbt pipelines, metric definitions, scheduled jobs and data quality checks - all managed as code in GitHub.
• Own the team's Claude skills: build, test, document and continuously improve them so results are reliable and consistent across users.
• Build monitoring and alerting on operational and business metrics, and investigate anomalies end to end.
• Support Finance with billing and invoicing data - validation, reconciliation and automation.
• Analyze large, complex datasets to answer product and business questions, and present findings clearly to technical and non-technical stakeholders.
• Partner with Product and R&D on data requirements for new features and integrations.

• Degree in Industrial Engineering, Computer Science, Statistics, Economics or another quantitative field.
• 3+ years of experience as a data / BI / product analyst or analytics engineer.
• Advanced SQL - must. Experience with Snowflake or a similar cloud data warehouse - strong advantage.
• Python for data analysis, automation, and tooling.
• Comfortable working in Git / GitHub - branches, pull requests, code review.
• Hands-on experience building with LLMs for data work - prompts, skills, agents or tools over structured data - strong advantage.
• Experience with dbt or similar data modeling tools - advantage.
• Experience with a BI tool and data modeling for reporting.
• Excellent communication skills in English.
• Familiarity with payments, chargebacks or fintech - advantage.