Company
Technology
DataEngineer,Product
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Engineer, Product. Skills: ETL/ELT, Apache Spark, Python, SQL, Data Pipelines. 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 ”
Industry & Context.
scalability; performance; data reliability
Location autonomy.
What They're Looking For.
Must Have
5+ years of experience in Data Engineering or a similar role within data-intensive or product-driven environments. 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. ability to collaborate with Machine Learning and product teams in ML-driven environments. problem-solving mindset with attention to scalability, performance, and data reliability.
Nice to Have
Familiarity with Databricks is a plus. Experience with streaming technologies (e. g. , Kafka, Flink), feature stores, or ML data workflows is highly desirable.
What You'll Do.
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 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.
How You'll Work.
Team & Collaboration
Collaborate closely with ML Engineers and product teams to shape data foundations that directly influence user experience and business performance. Ability to collaborate with Machine Learning and product teams in ML-driven environments.
Full Job Description
## Description This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer, Product based in Spain. This role sits at the core of a high-impact data and machine learning ecosystem, powering the systems that drive user engagement, personalization, and product growth at scale. You will be responsible for building and maintaining robust data pipelines that transform large-scale datasets into reliable inputs for ML models and recommendation systems. The environment is highly collaborative, working closely with ML Engineers and product teams to shape data foundations that directly influence user experience and business performance. You will take full ownership of end-to-end data workflows, ensuring scalability, reliability, and performance across complex data systems. The team operates in a lean, high-ownership setup where engineers are expected to drive solutions from design through to production. This is a hands-on role with strong exposure to machine learning-driven product development in a fast-scaling digital marketplace. ## 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 buil
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