Key skills
AirflowPySparkPythonRustScalaSparkAIMLLLMData Engineering
About this role
Role Overview
- Own, Design, Build & Operate Data Pipelines – Take responsibility for our Spark-based pipeline, from development through production and monitoring.
- Advance our ML Models – Improve and productionise models for AdTech use-cases such as lookalike modelling and demographics modeling.
- AI-Powered Productivity – Leverage LLM-based code assistants, design generators, and test-automation tools to move faster and raise the quality bar.
- Share your workflows with the team
- Drive Continuous Improvement – Profile, benchmark, and tune Spark workloads, introduce best practices in orchestration & observability, and keep our tech stack future-proof.
Requirements
- Bachelor/Master/PhD in Computer Science, Data Engineering, or a related field and 5+ years of professional experience.
- Expert-level Python and PySpark/Scala Spark experience
- Proven track record building resilient, production-grade data pipelines with rigorous data-quality and validation checks.
- Data-platform skills: operating Spark clusters, job schedulers, or orchestration frameworks (Airflow, Dagster, custom schedulers).
- (Plus) Working knowledge of ML lifecycle and model serving; familiarity with techniques for audience segmentation or look-a-like modelling is a big plus.
- (Plus) Rust proficiency (we use it for backend services and compute-heavy client-side modules).
Tech Stack
- Airflow
- PySpark
- Python
- Rust
- Scala
- Spark
Benefits
- Competitive rates
- Ownership instead of simply an executor
- An amazing and fun team that is distributed all over Europe.