Senior Data Platform Engineer in Berlin

European Tech Recruit are working closely with an exciting AI company committed to advancing research and creating technical solutions that enable safe-by-design AI systems.

They are looking for a talented

Senior Data Platform Engineer to join them on a hybrid basis in Berlin.

In this high-impact role, you will bridge the gap between cutting-edge AI research and high-performance engineering, treating the data platform as an internal product with research teams as your primary customers.

You will be responsible for building automated, petabyte-scale data processing pipelines and ensuring they are efficient, reliable, reproducible and fully traceable.

Responsibilities as

Senior Data Platform Engineer

• Scale and automate data processing infrastructure to handle petabytes of data while ensuring reliable day-to-day operation.
• Design execution layers for pipeline stages with different computational requirements, selecting appropriate processing engines and maintaining efficient end-to-end throughput.
• Optimise the use of compute resources across large-scale workloads, including GPU resources for compute-intensive data processing tasks.
• Build reliable pipelines with graceful failure recovery, restartability and comprehensive observability.
• Identify and diagnose performance bottlenecks, attributing compute usage and costs to individual pipeline stages.
• Establish robust systems for dataset versioning, lineage, reproducibility and traceability across all stages of the data processing lifecycle.
• Ensure intermediate outputs and final datasets can be reliably reproduced for specific and evolving research requirements.
• Develop and maintain appropriate documentation and data specifications in line with internal data governance requirements.
• Partner closely with Research and Engineering teams to ensure datasets integrate seamlessly with model training pipelines.
• Evaluate and introduce new technologies and approaches where they can improve scalability, reliability, performance or cost efficiency.

Requirements

• A bachelor's degree in a relevant field (e. g., computer science, computer engineering, software engineering) is required.
• 5+ years of experience designing, implementing, and managing large-scale distributed data processing systems, or working within large-scale distributed ML data frameworks, with recent experience using e. g.

Ray, Apache Spark, workflow orchestrators, Apache Arrow, and/or Parquet.

• Demonstrated ownership of a data processing system under real throughput, reliability, and cost pressure.

The specific frameworks matter less to us than evidence that you have had to reason about where a large pipeline breaks and why.

• Experience profiling and optimizing throughput and cost across heterogeneous workloads, including GPU-accelerated stages.
• Experience with dataset versioning, lineage, and reproducibility tooling.
• Ability to collaborate effectively with cross-functional teams, document best practices, and stay updated with the latest advancements in large-scale data processing and software development.
• Experience with workload managers (e. g., Ray, Kubernetes, Slurm).
• Familiarity with containerization tools (e. g., Docker, Enroot).
• Familiarity with data infrastructures and platforms (e. g., vector databases).

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