Software Specialist – Ft. Meade, MD


Candidate must have the following capabilities:
– 
Hadoop file service (hdfs) – experience with developing map-reduce jobs that process > lOOGBs of data, trouble shooting multiple parallel processes, tuning multiple parallel processes operating within specified constraints and resources. Experience in deploying, running, and troubleshooting hdfs.
– Apache Accumulo – experience developing/deploying/debugging/tuning an application on top of a multi-tenant Accumulo environment. Familiarity with trade-offs in configuration options of Accumulo based on workload and specified performance characteristics.
– Experience with networking design of compute cluster environments including IP address schemes, routing, Firewalls, load balancing, Ethernet Channel (Bonding), and troubleshoot ing of network hardware and software applications.
– Strong Computer Science fundamentals (data structures and algorithms). Understanding of distributed data structures, probabilistic data structures, experience either in developing/debugging/tuning data driven applications or deploying large scale data driven applications.
– Extensive Linux experience to include configuration management and system provisioning tools. In particular to include experience using some combination of slrace, dstat, perf, perl top, vmstat, netstat, etc common linux tools for debugging and performance analysis in a distributed environment
– Knowledge and understanding of common java deployment and configuration for J VMs.
– Experience working with long running jvm processes. Experience with tuning and/or troubleshooting.

Required Criteria:
– A minimum of 2-years Hadoop experience in any of the following: Apache Hadoop, Cloudera Distribution of Hadoop (CDH) or Hortonworks Hadoop Distribution (HDP)
– A minimum of 1 -year of developing applications for and experience with Apache Accumulo

Desired Criteria:
– Experience developing applications that run on moderate to large (200 to 1200+) node Hadoop clusters
– Experience configuring, monitoring, and tuning Hadoop 2 clusters
– Experience recovering from hardware failures that effect the Hadoop NameNode processing and file structures

 

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