DataEngineer,Product

## Accountabilities Design, build, and maintain scalable ETL/ELT data pipelines that transform raw data into high-quality datasets for machine learning and product use cases. Develop and optimize data transformation workflows supporting feature engineering for both offline model training and online inference systems. Collaborate closely with ML Engineers to understand data requirements and deliver reliable inputs for recommendation systems and predictive models. Ensure strong data quality, governance, and monitoring across all pipelines to guarantee accuracy, reliability, and consistency of datasets. Own data pipelines end-to-end, including design, implementation, deployment, monitoring, and continuous improvement. Improve performance, scalability, and efficiency of large-scale data processing systems, including batch and near real-time workloads. Contribute to building robust data foundations that enable experimentation, personalization, and ML-driven product innovation. Requirements: 5+ years of experience in Data Engineering or a similar role within data-intensive or product-driven environments. Strong hands-on experience with Apache Spark and Python for large-scale data processing and transformation. Solid knowledge of SQL and experience designing and working with data models and transformation logic. Proven experience building and maintaining ETL/ELT pipelines with end-to-end ownership. Experience working with high-volume data systems, including batch and/or near real-time processing pipelines. Strong ability to collaborate with Machine Learning and product teams in ML-driven environments. Familiarity with Databricks is a plus. Experience with streaming technologies (e.g., Kafka, Flink), feature stores, or ML data workflows is highly desirable. Strong problem-solving mindset with attention to scalability, performance, and data reliability. Benefits: Competitive salary aligned with senior data engineering market standards (€64,800–€74,400 annually referenced in