Catawiki was founded in 2008. It was originally designed as a website where collectors could manage and keep track of their collections online. Visitors can still add new items to the existing catalogue of collectibles. Unsurprisingly, the name Catawiki is a combination of the words ‘catalogue’ and ‘wiki’. In 2011, Catawiki began hosting weekly auctions in various categories, including art, antiques, classic cars, watches, jewellery, fashion, books, and stamps.
At Catawiki, data sits at the core of our decision-making, powering everything from commercial strategy and analytics to machine learning, AI, and performance marketing. The Data Engineering role exists to ensure this data foundation is robust, scalable, and ready to support a fast-growing global marketplace.
You’ll join a highly collaborative engineering environment, working closely with Machine Learning Engineers, Platform Engineers, and Backend Engineers. The team is responsible for building and evolving the data ecosystem that enables teams across Catawiki to explore, experiment, and innovate with confidence.
The scope of the role is intentionally broad, sitting at the intersection of data engineering, data platform engineering, and machine learning enablement. As Catawiki continues to grow, we’re expanding our data engineering capabilities to help scale the business, support more advanced use cases, and keep data a true competitive advantage.
What You’ll Do
Build and Scale Data Pipelines: Maintain and develop reliable batch and streaming pipelines that ingest data from internal systems and third-party sources into Catawiki’s data warehouse.
Empower Data Science and AI: Maintain and enhance the tools and platforms used by Data Scientists for analysis, experimentation, model training, and model deployment.
Protect Data and Privacy: Ensure data is stored securely and that governance, access control, and privacy standards are consistently applied across the data platform.
Run and Evolve the Data Platform: Maintain the infrastructure that hosts our data tools and applications, keeping it scalable, stable, and cost-effective.
Own Core Data Tooling: Self-host and operate key data engineering tools such as Airflow and Airbyte on Kubernetes.
Keep the Lights On: Provide operational support to ensure pipelines, platforms, and tools run smoothly and reliably for teams across the business.
Experienced Data Engineer: You have 3+ years of hands-on experience building and operating data systems in production.
Strong in Python, SQL & Data Integration: You’re fluent in Python and SQL and have experience with data integration tools such as Fivetran and/or Airbyte.
Infrastructure & DataOps Minded: You have experience with CI/CD, Infrastructure as Code (e.g. Terraform), and modern DataOps practices.
Cloud & Platform Savvy: You’ve worked with cloud platforms (GCP is a plus) and are familiar with parts of our data stack such as BigQuery, PubSub, DataFlow, GKE, Airflow, Airbyte, FastAPI and Prometheus.
Comfortable with Streaming & Scale: You have experience with streaming pipelines using technologies like Kafka, Pub/Sub, Dataflow, or Apache Beam.
Curious, Collaborative & Privacy-Aware: You’re keen to learn new tools, support data platform and machine learning engineering initiatives, and understand the importance of data privacy and GDPR.
Where You’ll Be
The role is based in the Netherlands (Amsterdam) with a hybrid arrangement (at least 2 days in the office per week).
Our Offices and Way of Working
Our vibrant offices in Amsterdam, Paris and Lisbon are designed to inspire collaboration. Most Catawikians operate in a hybrid setup, combining office-based and remote work, with a minimum of two days per week in the office, unless a role is explicitly stated as fully remote or fully office-based.
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