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.

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