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Responsible for designing, developing, and optimizing data processing solutions using Big Data technologies. Focus on building scalable and efficient data pipelines for handling large datasets and enabling batch & real-time data streaming and processing.
Job Responsibility:
Develop Spark applications using Scala or Python (Pyspark) for data transformation, aggregation, and analysis
Develop and maintain Kafka-based data pipelines including designing Kafka Streams and setting up Kafka Clusters
Create and optimize Spark applications using Scala and PySpark
Integrate Kafka with Spark for real-time processing
Collaborate with data teams including data engineers, data scientists, and DevOps to design and implement data solutions
Tune and optimize Spark and Kafka clusters for high performance, scalability, and efficiency
Write clean, functional, and optimized code adhering to coding standards
Troubleshoot and resolve issues related to Kafka and Spark applications
Maintain documentation for Kafka configurations, Spark jobs, and other processes
Stay updated on technology trends in functional programming, big data, and related technologies
Requirements:
Proficiency in Hadoop ecosystem big data tech stack (HDFS, YARN, MapReduce, Hive, Impala)
Proficiency in Spark (Scala, Python) for data processing and analysis
Proficiency in Kafka for real-time data ingestion and processing
Knowledge of ETL processes and data ingestion tools
Deep hands-on expertise in Pyspark, Scala, Kafka
Programming Languages: Scala, Python, or Java for developing Spark applications
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