About the role
Area Lead Analytics Engineer (Limited-time Relocation
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Bonus) Vilnius Kaunas Data Science & Analytics Multiple locations, Lithuania Vinted Marketplace Brief info about Vinted 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. The Vinted Group is made up of three business units that support this mission: Vinted Marketplace 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. Vinted Go 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. Vinted Pay 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. 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. Information about the position We are looking for an Area Lead Analytics Engineer to provide strategic technical leadership and shape the long-term direction of analytics engineering across the Revenue, Purchase, and Orders domains. In this role, you will act as a technical lead across multiple domains,helping build a scalable, reliable, cost-efficient, and future-proof data ecosystem. This is a highly technical Individual Contributor role for someone who enjoys balancing hands-on engineering with strategic architectural leadership. You will define architectural standards, strengthen data modeling practices, reduce systemic complexity, and ensure that data products and pipelines are built for sustainable evolution. In addition, you will have the opportunity to drive strategic technical change and consistency that scales in the wider data science and analytics function. You will work with a talented team of analytics engineers, Data Scientists, Engineering Managers, Product Managers, and other technical stakeholders and will sit at the intersection of hands-on technical leadership, architectural governance, strategic direction-setting, and cross-domain enablement. In this position, you’ll Set the long-term technical direction for analytics engineering across the Revenue, Purchase, and Orders domains. Identify key intersections across Revenue, Purchase, and Orders, ensuring they are reflected consistently in data models and architecture. Own architectural integrity, data modeling standards, reliability, and operational quality across the area. Act as a central technical point of contact for cross-domain initiatives, helping teams align on technical decisions and priorities. Create reusable patterns, shared standards, and a common design language for analytics engineering across domains. Drive DSA-wide improvements in data product quality, maintainability, documentation, ownership, observability, and operational excellence. Reduce and govern technical debt to enable sustainable, predictable evolution of data products and pipelines. Translate ambiguous business and technical challenges into clear technical direction, trade-offs, and actionable next steps. Mentor Analytics Engineers and other ICs through technical guidance, design reviews, code reviews, and architectural feedback. Communicate architectural decisions, standards, and long-term implications clearly to technical and non-technical stakeholders. About you You are an experienced Analytics Engineer, Data Engineer, or technical IC who enjoys building data systems that are not only technically strong, but also understandable, maintainable, and useful for the teams who rely on them. You combine deep technical expertise with strategic thinking. You can zoom into implementation details when needed, but you are equally comfortable stepping back to identify systemic issues, long-term risks, repeated patterns, and opportunities to improve how teams build data products across DSA. You likely bring: Strong experience in Data Engineering, Analytics Engineering, or related technical data roles. Hands-on experience designing, building, and maintaining production-grade data models, pipelines, or analytical data products at scale. Strong understanding of analytics engineering principles, including testing, documentation, lineage, data quality, observability, reliability, and operational excellence at scale. Demonstrated ability to define technical standards, reusable patterns, and architectural guardrails that improve quality and consistency across teams. Strong strategic and systems-thinking mindset, with the ability to spot dependencies, intersections, long-term risks, and repeated patterns across complex business areas. Ability to influence technical direction across multiple teams or domains without relying only on formal authority. Comfort working with ambiguity and translating complex problems into clear technical direction, standards, and next steps. Ability to balance local delivery needs with long-term architectural integrity and DSA-wide impact. Strong communication skills, with the ability to explain architectural trade-offs, technical decisions, and strategic priorities clearly. A collaborative mentoring mindset and a passion for raising the technical bar for other ICs. Excellent written and spoken English. Skills we value Strong expertise in SQL, data modeling, and semantic layer design, with hands-on experience on dbt or similar transformation frameworks. Experience working with cloud data platforms (GCP, AWS, Azure, or similar) Experience building data products on modern data stack technologies such as BigQuery, Snowflake, Redshift, Iceberg, or similar. Strong practical knowledge of query performance, and cloud cost governance. Experience with orchestration tools such as Airflow, Dagster, or similar. Foundational understanding of event-driven data systems. Comfortable working with Feature Stores, and ML facing workloads. Experience with BI and reporting tools such as Power BI, Tableau, Looker, or similar.
