As a Data Engineer with PySpark, you will:
* Design, develop, and maintain scalable and reliable data processing solutions using PySpark.
* Build and manage robust batch and streaming data pipelines.
* Develop efficient data transformation and processing solutions using Python, PySpark, and SQL.
* Design, develop, and optimize data models to support scalable and high-performance data processing.
* Work with large datasets and complex data processing workloads.
* Monitor, troubleshoot, and optimize data pipelines for performance, reliability, and scalability.
* Implement data quality, validation, and error-handling mechanisms across data pipelines.
* Collaborate with engineering, architecture, and business teams to deliver reliable data solutions.
* Contribute to Agile development practices and continuously improve data engineering standards and processes. What You Bring to the Table:
* 5+ years of professional experience in Data Engineering or a related field.
* Strong hands-on experience with PySpark as a core data processing technology.
* Strong proficiency in Python, PySpark, and SQL.
* Hands-on experience developing and maintaining batch and streaming data pipelines.
* Experience working with large-scale data processing and transformation.
* Good understanding of data modeling, data integration, and data optimization.
* Experience with cloud-based data platforms and modern data engineering architectures.
* Experience with data quality, validation, troubleshooting, and performance optimization.




* Familiarity with CI/CD and modern software engineering practices.
* Experience working in Agile development and delivery environments.
* Strong communication, collaboration, analytical, and problem-solving skills. You Should Possess the Ability to:
* Develop scalable and high-performance data pipelines using PySpark.
* Build efficient data transformations using Python, PySpark, and SQL.
* Process and manage large volumes of structured and unstructured data.
* Optimize Spark jobs, data processing performance, and resource utilization.
* Troubleshoot complex data pipeline and processing issues.
* Design reliable and maintainable data engineering solutions.
* Implement data quality and validation processes.
* Work effectively with technical and business stakeholders.
* Apply best practices for code quality, scalability, maintainability, and reliability.
* Continuously improve data engineering processes and solutions. What We Bring to the Table:
* Opportunity to work on enterprise-scale data engineering initiatives in Spain.
* Exposure to modern data platforms, PySpark, cloud technologies, and large-scale data processing.
* A collaborative Agile environment focused on technical excellence and innovation.
* Opportunities to work on complex data pipelines and data transformation solutions.
* Continuous learning and opportunities for technical and professional growth.
* A culture focused on quality, ownership, scalability, and sustainable data engineering solutions.

📌 Data Engineer with Pyspark (Comunidad de Madrid)
🏢 STAFIDE
📍 Comunidad de Madrid