We are seeking a highly experienced Senior Hadoop Engineer to lead the design, development, and optimization of our large-scale data processing and analytics environment. The ideal candidate will have extensive hands-on expertise in Hadoop ecosystem tools and distributed data frameworks. This role involves working closely with data architects, analysts, and application teams to build scalable and secure big-data solutions that support business-critical analytics.
-
Design, build, and maintain Hadoop-based big data platforms and data pipelines.
-
Implement and optimize large-scale data processing applications using tools such as HDFS, Hive, Spark, Impala, and HBase.
-
Work with engineering and business teams to translate requirements into scalable data architecture.
-
Improve performance and reliability of Hadoop clusters, including monitoring, capacity planning, and tuning.
-
Develop and manage ETL processes that integrate data from multiple sources.
-
Ensure data security, governance, and compliance across all Hadoop environments.
-
Automate operational tasks and support continuous deployment practices.
-
Troubleshoot issues across Hadoop components and provide root-cause analysis.
-
Support migration and modernization initiatives to cloud platforms when applicable.
-
12+ years of professional experience in data engineering or software engineering roles.
-
Strong expertise in Hadoop ecosystem tools including HDFS, YARN, Hive, Pig, Spark, Kafka, Sqoop, Oozie, and Zookeeper.
-
Proficiency in programming languages such as Java, Scala, and Python.
-
Solid understanding of distributed systems, parallel processing, and performance optimization.
-
Experience working with relational and NoSQL databases (e.g., Oracle, MySQL, HBase, Cassandra, MongoDB).
-
Hands-on experience with data ingestion and ETL pipelines.
-
Experience with version control, CI/CD tools, and Linux environments.
-
Familiarity with cloud platforms such as AWS, Azure, or GCP is preferred.
-
Strong analytical, problem-solving, and communication skills.