The Hadoop Administrator provides leadership, mentoring and guidance in the following areas:
1) Hadoop System design
2) strategy and vision
3) Hadoop standards and best practices
4) production support
5) Unix scripting
6) Hortonworks administration and
7) Big Data techniques.
The Hadoop Administrator is responsible for comprehending Hadoop architecture, design and maintenance as well as providing solutions through development, thorough testing and documentation.
Required Skills / Qualifications:
Bachelors degree with a major in computer science, accounting, business, healthcare, mathematics, or related field.
Minimum 5 years of Hadoop administration and/or systems level experience.
Experience with HIVE, Spark, Kafka, HBASE, Solr, Ambari, Ranger, Grafana, etc...
Experience with open source configuration management and deployment tools such as Puppet or Chef and Shell scripting.
Experience with DevOps tools like Ansible, cheff and HAProxy.
Responsible for implementation and ongoing administration of Hadoop infrastructure Aligning with the systems engineering team to propose and deploy new hardware and software environments required for Hadoop and to expand existing environments Working with data delivery teams to setup new Hadoop users. This job includes setting up Linux users, setting up Kerberos principals and testing HDFS, Hive, HBase and Yarn access for the new users Responsible for setting up and manage kafka clusters on linux enronments.
Performance tuning of Hadoop clusters and Hadoop MapReduce routines Screen Hadoop cluster job performances and capacity planning Monitor Hadoop cluster connectivity and security Responsible for performing Hadoop updates, patches, version upgrades when required Work with Vendor support teams on support tasks General operational expertise such as good troubleshooting skills, understanding of system's capacity, bottlenecks, basics of memory, CPU, OS, storage, and networks Ability to communicate effectively with customers, peers and management and be able to handle multiple tasks at one time.