JD: Develop and maintain scalable data pipelines using PySpark and Spark SQL for processing large datasets efficiently. Write clean, reusable, and optimized code in Python for data manipulation, analysis, and automation tasks. Design and implement ETL workflows to extract, transform, and load data from various structured and unstructured sources. Collaborate with data engineers, analysts, and stakeholders to understand data requirements and deliver solutions. Optimize Spark jobs for performance tuning, resource utilization, and minimizing execution time. Work with distributed computing frameworks to process and analyze big data in a cloud or on-premises environment. Develop and maintain unit tests to ensure the accuracy and reliability of data pipelines and transformations. Utilize Spark SQL for querying and managing large datasets stored in distributed systems like Hadoop or cloud storage. Monitor and troubleshoot data pipeline issues, ensuring reliability and timely delivery of data. Stay updated with the latest advancements in PySpark, Spark SQL, and big data technologies to improve existing systems.
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