We are seeking a skilled and motivated Big Data Engineer to design, develop, and maintain scalable data processing systems. The ideal candidate will have strong experience in the Hadoop ecosystem, ETL pipeline development, and real-time data streaming frameworks. You will play a key role in building robust data infrastructure and ensuring high-performance data processing across large-scale distributed systems.

Design, develop, and maintain scalable big data pipelines and architectures Build and optimize ETL workflows using tools like Apache NiFi Work with large datasets using Hadoop ecosystem tools such as HDFS, Hive, Spark, MapReduce, and Impala Develop data processing applications using Python, PySpark, Java, or Scala Implement and manage real-time streaming pipelines using Apache Kafka Debug and resolve production issues in data pipelines and Hadoop/Hive jobs Optimize performance of data processing jobs and queries Administer and manage Hadoop clusters using Cloudera Collaborate with cross-functional teams including data scientists, analysts, and DevOps engineers Create and maintain technical design documentation and process workflows Ensure data quality, reliability, and system scalability

Strong experience with big data technologies: Hadoop, Spark, Sqoop Hands-on experience with streaming frameworks like Kafka Expertise in designing ETL pipelines using Apache NiFi Proficiency in object-oriented programming: Python, Java, Scala, PySpark In-depth knowledge of the Hadoop ecosystem (HDFS, YARN, MapReduce, Hive, Impala) Strong command of Linux/Unix systems, shell scripting, and SQL Experience in building large-scale distributed systems Ability to troubleshoot and debug complex production issues Strong analytical and problem-solving skills Experience working with Cloudera for Hadoop administration