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Urgent! Storage (HDFS) Engineer Job Opening In Singapore, Singapore – Now Hiring MTS SYSTEMS ENGINEERING PTE. LTD.

Storage (HDFS) Engineer



Job description

Key Responsibilities:

  • Design, deploy, and maintain HDFS clusters to support enterprise-scale data applications.

    (Please include and/or provide your past experiences in this aspect in your resume).

  • Monitor cluster performance, manage storage capacity, and ensure high availability and fault tolerance.

  • Implement data security, access controls, and encryption for HDFS data.

  • Troubleshoot and resolve issues related to HDFS, including data node failures, replication issues, and performance bottlenecks.

  • Manage data ingestion pipelines and optimize data storage formats (e.g., Parquet, Avro).

  • Support and work with data engineering and analytics teams to ensure reliable data delivery and transformation workflows.

  • Automate cluster operations using scripting (e.g., Bash, Python) and orchestration tools.

  • Conduct upgrades and patching of Hadoop ecosystem components (HDFS, YARN, Hive, etc.).

  • Maintain documentation of architecture, configurations, and best practices.

  • Ensure compliance with data governance and data privacy policies.

Qualifications:

Required

Bachelor’s degree in Computer Science, Information Systems, or a related field.

3–5+ years of experience with Hadoop ecosystem, particularly HDFS administration.

Strong understanding of HDFS architecture, replication, and fault tolerance.

Experience with Cloudera, Hortonworks, or Apache Hadoop distributions.

Proficiency in Linux/Unix system administration and scripting (Bash, Python, etc.).

Familiarity with related components: YARN, Hive, HBase, Spark, Oozie, and Zookeeper.

Experience with monitoring tools like Ambari, Cloudera Manager, or Nagios.

Preferred

  • Hadoop certification (e.g., Cloudera Certified Administrator for Apache Hadoop - CCAH).

  • Knowledge of cloud-based big data platforms (AWS EMR, Azure HDInsight, GCP Dataproc).

  • Experience with containerization (Docker/Kubernetes) for big data workloads.

  • Exposure to data lake architectures and data governance tools.

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Required Skill Profession

It & Technology



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