High-Performance Data Engineer
2 days ago
We are seeking a skilled PySpark Data Engineer to drive the development of robust data processing and transformation solutions within our data platform.
Key Responsibilities:- Data Engineering Development:
- Design, develop, and test PySpark-based applications to process, transform, and analyze large-scale datasets from various sources.
- Implement efficient data transformation and aggregation using PySpark and relevant big data frameworks.
- Develop robust error handling and exception management mechanisms to ensure data integrity and system resilience within Spark jobs.
- Optimize PySpark jobs for performance, including partitioning, caching, and tuning of Spark configurations.
- Data Analysis and Transformation:
- Collaborate with data analysts, data scientists, and data architects to understand data processing requirements and deliver high-quality data solutions.
- Analyze and interpret data structures, formats, and relationships to implement effective data transformations using PySpark.
- Work with distributed datasets in Spark, ensuring optimal performance for large-scale data processing and analytics.
- Data Integration and ETL:
- Design and implement ETL (Extract, Transform, Load) processes to ingest and integrate data from various sources, ensuring consistency, accuracy, and performance.
- Integrate PySpark applications with data sources such as SQL databases, NoSQL databases, data lakes, and streaming platforms.
Strong PySpark development skills and experience with big data technologies are required. The ideal candidate should be able to work in a fast-paced, data-driven environment.
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