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.

Let’s Connect:

Want to discuss this opportunity in more detail? Feel free to reach out.

Recruiter: Hema Murali
Phone: +31 20 369 0609 ; Extn :148
Email: [email protected]
LinkedIn: https://www.linkedin.com/in/hema-murali-315999329/

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