Key Responsibilities

• Build and scale real-time streaming data pipelines supporting production ML systems.
• Develop and enhance feature engineering infrastructure used by Data Scientists.
• Improve data quality through monitoring, validation and governance.
• Optimise large-scale ETL and streaming workloads for performance and reliability.
• Collaborate closely with Data Science and Backend Engineering teams.
• Build ingestion frameworks for new and evolving data sources.
• Help shape the future architecture of the company's cloud data platform.
• Take ownership of technical decisions and platform scalability.

Qualifications

• 5+ years of commercial Data Engineering experience building production data platforms.
• Strong experience with Apache Flink, Kafka, and real-time streaming architectures.
• Professional Java development experience, with Go or Python considered a bonus.
• Experience processing TB-scale datasets and high-throughput event streams.
• Hands-on experience with AWS, Airflow, dbt, Terraform, and Kubernetes.
• Experience working with Machine Learning or Data Science teams in production environments.
• Understanding of Data Lakes, Feature Stores, Lakehouse architecture, and modern data modelling.
• Comfortable owning architecture and working across multiple engineering teams.

Location: Hamburg, Germany (Hybrid, 3 days onsite) with full relocation support available.

If you're excited by the opportunity to build the data platform behind AI systems serving hundreds of millions of users worldwide, while owning architecture and solving genuinely complex engineering challenges at scale, we'd love to hear from you.

Requirements added by the job poster

• 5+ years of work experience with Java

• 5+ years of work experience with Apache Flink