Backend Engineer(Hadoop)

• Design, develop, and maintain scalable backend applications and data processing services using Java/Spring Boot and related technologies.
• Develop and support distributed data processing solutions using the Hadoop ecosystem, including HDFS, Hive, Spark, and related components.
• Build REST APIs, backend services, and data integration components to support enterprise applications and data platforms.
• Develop efficient ETL/data processing pipelines and optimize large-volume data processing jobs.
• Work with Hadoop/Hive/Spark to ingest, transform, process, and analyze large datasets.
• Troubleshoot application, data processing, performance, and production issues across backend and Hadoop environments.
• Implement code quality, unit testing, logging, monitoring, and performance optimization practices.
• Collaborate with architects, data engineers, application teams, and business stakeholders to understand requirements and deliver solutions.
• Participate in deployment, release management, production support, and incident resolution activities.
• Ensure solutions follow enterprise security, coding, data governance, and technology standards.

Key Skills:

• Strong experience in Java, Spring Boot, REST APIs, and backend development.
• Hands-on experience with Hadoop ecosystem, particularly HDFS, Hive, Spark, and YARN.
• Good knowledge of SQL, data processing, ETL, and distributed computing.
• Experience with Kafka or other messaging technologies is an advantage.
• Exposure to Linux/Unix, Git, CI/CD, and cloud platforms is preferred.
• Strong troubleshooting, analytical, and problem-solving skills.
• Experience working in large-scale enterprise or banking environments is preferred.
• Typically 5–8 years of relevant backend development experience, with solid hands-on Hadoop experience.