Product Analyst

Due to continuous growth, our Product team is excited to welcome a highly analytical Product Analyst to help us align data-driven decision-making with business goals across three products at once, each at a different stage. As Product Analyst, you'll operate on the intersection of product analytics and business analysis: you don't just explain why a funnel underperforms, you translate that into what it means in euros, risk, and compliance terms. You'll work in a fully MCP-equipped, AI-native environment, own the event taxonomy across all three products, automate recurring reporting so you have the time to go genuinely deep on each product, and act as the go-to person for the Growth team on everything A/B testing. Working closely with Product Managers, Engineering, Growth, SEM, and Payments, you'll have the opportunity to make a meaningful, structural impact — not just on individual metrics, but on how the whole team thinks about data. If you're passionate about solving complex problems, taking full ownership without waiting to be asked, and driving results across multiple products at once, this role is perfect for you! Key responsibilities: Product analytics core • Independently run funnel, cohort, and retention analysis in Amplitude across all three products. • Design and interpret experiments (A/B tests), with a clear eye for primary vs. diagnostic metrics. • Go deeper than headline numbers through segmentation (by country, user segment, acquisition channel, etc.). Go-to person for Growth on A/B testing • Be the single point of contact for the Growth team on everything experiment-related: test design, sample size, choice of primary vs. diagnostic metrics, and interpretation of results. • Be consulted before a test goes live, not only once results come in — safeguarding experiment quality upfront, not just in hindsight. • Recognize and correct common experimentation pitfalls (peeking, dependent variants, poorly chosen success metrics) before they lead to a flawed rollout decision. Business and risk layer • Translate product metrics into unit economics: what a conversion drop costs in ARPPU, CAC, or margin. • Model scenarios (e.g. the impact of a price change on both conversion and downstream risk indicators). • Produce output that non-product stakeholders (Payments, SEM, Growth, Finance, C-level) can act on directly — not only PMs. Ownership: taxonomy & reporting • Own the event taxonomy across all products: proactively track which events are live or deprecated, document renames as they happen, and prevent outdated event names (in skills, dashboards, or AI prompts) from silently producing wrong conclusions. This is a recurring responsibility, not a one-off documentation task — every product release can affect the taxonomy. • Automate reporting to free up time for depth: turn recurring analyses (funnel health, KPI reviews, country splits) from manual pulls into self-running dashboards/skills, so stakeholders always have current numbers without needing to ask an analyst — and so the time freed up goes into genuinely deep analysis per product, not more of the same routine work. • Proactively dig into where things "leak": don't wait to be asked — actively look for underperforming segments or funnel steps, and for untapped business opportunities surfacing from the data, then raise them unprompted with PMs or the Project Director. Take the team by the hand, analytically • This is not a purely individual-contributor role: bring PMs (and other stakeholders) along in how you reach a conclusion, not just what the conclusion is, so they get better themselves at asking the right data question. • Actively spot and correct conclusions that the data doesn't support (e.g. correlation presented as causation, or an underpowered sample treated as significant). • Be the team's analytical conscience: when there's doubt about a number or interpretation, you're the person people turn to — and you take on that responsibility proactively rather than waiting to be asked. AI leverage • Work in an environment fully set up with MCPs (Amplitude, Databricks, Linear, Google Drive, etc.) — this is the default way of working, not an experiment on the side. • Build and maintain reusable Amplitude skills and analysis tooling. • Use LLMs actively for query generation, first-pass analysis, and reporting. • Critically evaluate and correct AI output: recognize when a model uses an outdated event name, incorrectly sums a non-additive metric, repeats a stale assumption, or draws a conclusion the underlying data doesn't support. • Know when not to trust AI: with ambiguous data, conflicting results, or output that sounds "too clean." Required skills: We're looking for someone who combines exceptional analytical depth with genuine ownership and the ability to bring others along. Here's what we're looking for: • Exceptional analytical ability — this isn't a "can write adequate SQL" role; you spot patterns others miss, untangle conflicting signals, and reach the right conclusion under time pressure. • Independent working knowledge of Amplitude or a comparable product-analytics tool (or demonstrable SQL proficiency to pick this up quickly). • Experience designing experiments (A/B testing) — able to explain what makes a good primary metric and why. • Comfortable with unit economics — CAC, LTV, ARPPU, margin — not as buzzwords but as a working model. • An AI-native way of working: daily use of LLMs throughout the analysis workflow, combined with a critical, verifying mindset. • A self-starter: capable of setting up your own tooling and workflow in an MCP-rich environment without needing direction — you don't wait for an instruction to build a skill or dashboard, you spot where automation frees up time and act on it. • Demonstrable proactivity: concrete examples of self-initiated analyses (not requested) that led to a real action or business insight. • An affinity for maintaining documentation and