Senior Manager – Data Engineering & Machine Learning

About this role

At Catawiki, every day brings the extraordinary! Whether it’s Daniel Ricciardo’s Formula 1 Car , a Woolly Mammoth’s Skeleton , Lady Gaga's Jumpsuit or Usain Bolt’s running shoe , we encounter exceptional objects every day. We’re a one-of-a-kind marketplace for buying and selling special objects. Each week, more than 100,000 unique items are auctioned, all carefully curated by our passionate in-house experts. Having sold over 25 million unique objects, our mission is to become the world’s most popular destination for special objects.

As a growing, diverse and sustainable scale-up, we proudly live by three core values. If these values resonate with you, we’d love to explore how you can join us. Taking ownership and driving impact Being open to change and feedback Being passionate about our mission and our customers. Your mission As Senior Manager – Data Engineering & Machine Learning, you’ll lead Catawiki’s established team of Data Engineers and Machine Learning specialists.

You’ll help the team grow, set its technical direction and build on work already underway across data foundations and production machine learning. The role spans two connected disciplines. You’ll bring deep experience in either Data Engineering or Machine Learning Engineering, and enough breadth to set direction across the other. You’ll partner with Product, Engineering, Analytics and business stakeholders to set priorities and connect the team’s work to outcomes across Catawiki.

What you'll do You’ll lead the team through platform modernisation and product-facing ML work. Current priorities include modernising the infrastructure for serving production models, introducing a feature store to improve feature management and consistency across model training and serving, and strengthening observability and cost controls. You’ll help set priorities and raise the reliability and impact of this work.

Lead and develop the team: Set direction, coach experienced specialists and create clear ownership across Data Engineering and Machine Learning. Build dependable data foundations: Improve the pipelines, integrations and infrastructure the wider Data team relies on for analysis, reporting and machine learning. Strengthen production ML: Improve deployment, monitoring and operational follow-through for production models, so teams can run them reliably and resolve issues effectively.