About the Role
<div class="content-intro"><h2><strong>Brief info about Vinted </strong></h2> <p data-renderer-start-pos="1"><span data-sheets-root="1">Our mission is to make second-hand the first choice, and we're looking for people who want to help us get there. Every day, we work together to help our members buy and sell pre-loved clothing and lifestyle items, giving each piece a second life – or even a third. <br>The Vinted Group is made up of three business units that support this mission:<br><br><strong><a href="https://careers.vinted.com/marketplace" target="_blank">Vinted Marketplace</a></strong> is Europe’s leading platform for second-hand fashion and a go-to destination for all kinds of pre-loved items, with a growing range of categories. Our platform connects millions of members across 20+ markets, helping great items find a new life.<br><br><a href="https://careers.vinted.com/vintedgo" target="_blank"><strong>Vinted Go</strong></a> enhances the shipping experience with a vast network of over 500,000 pick-up and drop-off points, partnering with more than 60 carriers across Europe, with added services like item verification for peace of mind on high-value pieces.<br><br><a href="https://careers.vinted.com/vintedpay" target="_blank"><strong>Vinted Pay</strong></a> is the newest part of the Vinted Group, dedicated to bringing secure, reliable payments to buyers and sellers across Europe. Seamlessly integrated into the Vinted app, it helps keep every transaction safe, efficient, and easy for our members.<br><br>Founded in 2008 in Lithuania, Vinted began as a way for friends to find new homes for clothes they no longer needed. In 2019, we became Lithuania's first unicorn! Today, our headquarters remain in Vilnius, and we've grown with offices across Europe, supported by a team of over 2,000 people. </span></p></div><h2><strong>Information about the position </strong></h2> <p>As an Analytics Engineer in Marketing, you’ll architect, scale, and optimize the business critical marketing data pipelines that teams rely on to optimize marketing performance and decision-making. Specifically, you will design robust, future-proof pipelines that ingest, process, and monitor data from diverse external sources to ensure our teams have access to high-integrity data. In this role, you’ll have the opportunity to build a deep understanding of the marketing domain while proactively identifying data-related process optimization opportunities.</p> <p>You’ll partner closely with:</p> <ul> <li><strong>Marketing Operations Intelligence</strong> and <strong>Marketing Modelling Intelligence</strong> teams , who use this data daily for performance reporting and model-driven optimisation.</li> <li><strong>Other Analytics Engineers in Marketing</strong>, with shared ownership of the marketing data domain</li> <li>A broader <strong>40+ person Analytics Engineering Guild across Vinted</strong>, where you’ll benefit from established patterns, code reviews, reusable dbt and python components, and regular knowledge sharing.</li> </ul> <p>This is an end-to-end role across <strong>BigQuery, dbt, Airflow</strong>: from raw ingestion and transformations through to trusted, well-documented datasets and domain-ready reporting.</p> <p>You will report to the Manager of Data Science & Analytics, Marketing Analytics Engineering.</p> <p>We have three distinct roles within Data Science & Analytics (DSA). We believe each role can make a similar business impact in different ways, and therefore our salary ranges are the same for all three roles. To understand your role within DSA context better, here are brief descriptions of each role we have in the department:</p> <ul> <li><strong>Analytics Engineers</strong> are responsible for data curation – translating data needs from stakeholders into architecting, building and maintaining efficient & reliable data models and pipelines.</li> <li><strong>Decision Scientists</strong> are responsible for actionable insights, identifying and sizing opportunities, and automated tools that increase the quality of product and business decisions by applying statistical methods and data-driven decision-making.</li> <li><strong>Data Scientists</strong> are responsible for identifying algorithmic opportunities, ensuring those opportunities are addressed optimally, and designing, developing, and maintaining production-grade statistical and machi