Job
Description
As a Big Data Engineer specializing in the Hadoop Ecosystem, your primary role will involve designing, deploying, and maintaining Hadoop ecosystem clusters (HDFS, YARN) to ensure stability, scalability, and security for production workloads. You will be responsible for developing and optimizing batch ETL pipelines using Spark, Hive, Sqoop, and related tools to ingest, transform, and store large datasets. Additionally, you will build and operate streaming data pipelines using Kafka to guarantee low-latency delivery and fault tolerance. Your duties will also include performance tuning, capacity planning, and cluster troubleshooting, such as resource contention, job optimization, and storage format tuning (Parquet/ORC/Avro). It will be essential for you to implement data reliability, lineage, and governance best practices and automate routine operations with Oozie/airflow and scripting. Collaboration with Data Engineering, Data Science, and DevOps teams will be necessary to productionize models, integrate CI/CD, and enable observability and alerting. Qualifications Required: - Apache Hadoop - HDFS - YARN - Apache Spark - Apache Hive - Apache Kafka - Sqoop - Oozie Preferred Qualifications: - AWS EMR or cloud-managed Hadoop services - Apache HBase - Apache Airflow You should possess a Bachelor's degree in Computer Science, IT, or equivalent practical experience, along with a demonstrated track record of delivering production Big Data solutions in Hadoop ecosystems. In this on-site role based in India, you will have the opportunity for hands-on ownership of large-scale data platforms, working across analytics and ML data pipelines. You will gain exposure to modern data engineering practices, performance tuning, and platform automation while being part of a collaborative engineering culture that emphasizes operational excellence and career growth. As a Big Data Engineer specializing in the Hadoop Ecosystem, your primary role will involve designing, deploying, and maintaining Hadoop ecosystem clusters (HDFS, YARN) to ensure stability, scalability, and security for production workloads. You will be responsible for developing and optimizing batch ETL pipelines using Spark, Hive, Sqoop, and related tools to ingest, transform, and store large datasets. Additionally, you will build and operate streaming data pipelines using Kafka to guarantee low-latency delivery and fault tolerance. Your duties will also include performance tuning, capacity planning, and cluster troubleshooting, such as resource contention, job optimization, and storage format tuning (Parquet/ORC/Avro). It will be essential for you to implement data reliability, lineage, and governance best practices and automate routine operations with Oozie/airflow and scripting. Collaboration with Data Engineering, Data Science, and DevOps teams will be necessary to productionize models, integrate CI/CD, and enable observability and alerting. Qualifications Required: - Apache Hadoop - HDFS - YARN - Apache Spark - Apache Hive - Apache Kafka - Sqoop - Oozie Preferred Qualifications: - AWS EMR or cloud-managed Hadoop services - Apache HBase - Apache Airflow You should possess a Bachelor's degree in Computer Science, IT, or equivalent practical experience, along with a demonstrated track record of delivering production Big Data solutions in Hadoop ecosystems. In this on-site role based in India, you will have the opportunity for hands-on ownership of large-scale data platforms, working across analytics and ML data pipelines. You will gain exposure to modern data engineering practices, performance tuning, and platform automation while being part of a collaborative engineering culture that emphasizes operational excellence and career growth.
