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 unstruc

📌 Data Engineer with Pyspark(ID: 4079) (Madrid)
🏢 STAFIDE
📍 Madrid