Data Engineer- PySpark & Python
Summary
The Data Engineer will design, develop, and optimize scalable data pipelines and ETL/ELT workflows using PySpark, Python, and Azure Data Factory. The role focuses on processing large-scale data within enterprise environments and collaborating with cross-functional teams to ensure data quality and performance.
We are seeking experienced Data Engineers with strong expertise in PySpark and Python to join our growing data engineering team in Kraków. The successful candidates will be responsible for designing, developing, and optimizing scalable data pipelines and data processing solutions for large-scale enterprise environments.
Experience with Azure Data Factory (ADF) is required, while hands-on experience with Azure Databricks (ADB) will be considered a significant advantage.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines using PySpark and Python.
- Build and optimize ETL/ELT workflows for processing large volumes of data.
- Write efficient, reusable, and high-performance code with a strong focus on optimization.
- Process and manage structured and unstructured data from multiple sources.
- Implement best practices related to data engineering, performance tuning, and code quality.
- Develop, orchestrate, and monitor data workflows using Azure Data Factory (ADF).
- Collaborate with Data Scientists, Data Analysts, Solution Architects, and other stakeholders.
- Ensure data quality, reliability, and integrity across all data platforms.
- Troubleshoot and resolve performance bottlenecks in data processing pipelines.
- Contribute to data architecture discussions and continuous improvement initiatives.
Required Skills & Experience
- 6–10 years of overall IT experience.
- 4–5 years of hands-on experience in PySpark and Python development.
- Strong understanding of data engineering principles and modern data pipeline architectures.
- PySpark (Spark SQL, DataFrames, performance tuning)
- Python scripting and application development
- Azure Data Factory (ADF) orchestration and pipeline development
- Strong knowledge of data optimization techniques, including partitioning, caching, joins, and query optimization.
- Experience working with large-scale distributed data processing systems.
- Strong analytical and problem-solving skills.
- Good understanding of SQL, database concepts, and data modeling.
Nice-to-Have Skills:
- Hands-on experience with Azure Databricks (ADB).
- Exposure to cloud technologies, preferably Microsoft Azure.
- Experience with CI/CD pipelines and DevOps practices for data engineering.
- Knowledge of Data Lake and Lakehouse architectures.
- Familiarity with modern data governance and monitoring practices.