Design, develop, and maintain large-scale distributed data processing systems using Hadoop, HDFS, MapReduce, and Spark. Build scalable data pipelines for batch and real-time processing using PySpark and Spark Streaming. Work with HBase to manage structured and semi-structured data storage, ensuring efficient querying and data integrity. Optimize and troubleshoot performance issues in distributed systems to ensurehigh availability and reliability. Write clean, maintainable, and efficient code using Python for data processing and automation tasks. Collaborate with data scientists, analysts, and other engineering teams to understand data requirements and deliver effective solutions. Implement best practices for data governance, security, and compliance. Monitor, troubleshoot, and improve ETL processes and workflows. Stay updated with emerging trends and technologies in big data and analytics. Desired Profile: Bachelors or Master s degree in Computer Science, Data Engineering, or a related field. 2+ years of hands-on experience with big data technologies like Hadoop, HDFS, HBase, and MapReduce. Strong programming skills in Python. Experience with designing and managing scalable, distributed data architectures. Hands-on experience with data integration, ETL development, and workflow orchestration tools. Solid understanding of database systems and data modeling Familiarity with cloud platforms (AWS, Azure, GCP) is a plus.