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Senior ETL Developer will be responsible for designing, implementing, and optimizing distributed data processing jobs to handle large-scale data in Hadoop Distributed File System(HDFS) using Apache Spark and Python. This role required deep understanding of data engineering principles, proficiency in Python and hands-on experience with Spark and Hadoop ecosystems. Developer will collaborate with data engineers, analysts, and business stakeholders to process, transform and drive insights and data driven decisions.
Job Responsibility
Design and Implement of Spark applications to process and transform large datasets in HDFS
Develop ETL Pipelines in Spark using Python for data Ingestion, cleaning, aggregation, and transformations
Optimize Spark jobs for efficiency, reducing run time and resource usage
Finetune memory management, caching, and partitioning strategies for Optimal performance
Load data from different sources into HDFS, ensuring data accuracy and integrity
Integrate Spark Applications with Hadoop frameworks like Hive, Sqoop etc.
Troubleshoot and debug Spark Job failures, monitor job logs, and Spark UI to Identify Issues
Requirements
5-8 years of relevant experience in Software Development
Experience in systems analysis and programming of software applications
Experience in managing and implementing successful projects
Working knowledge of consulting/project management techniques/methods
Ability to work under pressure and manage deadlines or unexpected changes in expectations or requirements
Bachelor’s degree/University degree or equivalent experience
What we offer
Medical, dental & vision coverage
401(k)
Life, accident, and disability insurance
Wellness programs
Paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays