Senior Data Platform Engineer

Ovoko is transforming Europe’s €30B+ used car parts market - a massive, fragmented industry still largely offline. We connect 7,000+ scrapyard owners, car parts sellers and dismantlers with millions of buyers through our marketplace, SaaS and logistics platform, making it effortless to trade parts across borders. With over 42 million parts listed, we’ve grown into one of Europe’s fastest-scaling e-commerce companies, but the real opportunity lies ahead, as we build a real category-defining platform worldwide.

ABOUT THE ROLE

As part of this journey, we are seeking an experienced Data Platform Engineer to build, evolve, and scale our Data Platform, supporting the business's growing needs across Analytics, Data Science, AI, and Product development. You’ll join our engineering focused Data team, partnering with Data Engineers and Analytics Engineers to build reliable foundations for data products and help teams across the company use data effectively.

Working closely with the company’s Infrastructure team, you will take ownership of the Data platform infrastructure and DataOps practices that support how data solutions are built, deployed, and operated. Your focus will be on Data platform reliability, infrastructure-as-code, CI/CD, observability, access management, and cost efficiency. You will also ensure the platform remains secure by embedding the company’s security standards and best practices into its infrastructure and day-to-day operations. By developing shared tooling and self-service workflows, you will help Data Engineers and Analytics Engineers deliver and operate data pipelines and models more efficiently, while shaping the architectural decisions that keep the platform scalable, maintainable, and adaptable to future needs.

• Ovoko is moving towards a truly data-driven organization, where Data & Analytics play a central role in product, operational, and strategic decision-making.

Build self-service tooling and CI/CD workflows for data code and infrastructure so Data Engineers and Analytics Engineers can independently create, test and deploy pipelines and dbt projects, with separate environments, automated testing and safe data publishing practices such as Write-Audit-Publish.

Deep hands-on experience operating and scaling streaming platforms and processing frameworks such as Kafka and Apache Flink, particularly for user behavior tracking—from handling traffic spikes at event collection endpoints to ensuring reliable downstream event processing and delivery. This includes managing consumer lag, backpressure, state, checkpointing, and replay strategies.

Experience designing and operating hybrid data platforms across on-premises and cloud environments, making informed decisions about where data is stored and processed to minimize unnecessary transfers and egress costs. Familiarity with incremental replication, CDC and upsert patterns, and their implications for network usage, latency and consistency would be particularly valuable.