• Competitive
  • Raleigh, NC, États-Unis
  • CDI, Plein-temps
  • Credit Suisse -
  • 18 juin 19

Big Data Support # 132031

We Offer
We are seeking hardworking, experienced Big Data Support to join a growing, high-visibility cross-Bank team that is developing and deploying solutions to some of Credit Suisse's most important analytical problems. As a member of our team, you will support big data clients with data spanning Credit Suisse's global organization to solve emerging challenges via the utilization of new technologies such as:
  • Distributed file systems and storage technologies (HDFS, HBase, Hive, Kudu)
  • Large-scale distributed data analytic platforms and compute environments (Spark, Map/Reduce)
  • Streaming technologies such as Kafka, Flume
  • Data Connection and Enrichment (Tamr)

Credit Suisse maintains a Working Flexibility Policy, subject to the terms as set forth in the Credit Suisse United States Employment Handbook.

You Offer
  • You have deep knowledge of Cloudera Hadoop components such as HDFS, Sentry, HBase, Impala, Hue, Spark, Hive, Kafka, Kudu, YARN, ZooKeeper and Postgres.
  • You are proficient in Python, Ansible, Salt and dev ops technologies. Linux and BASH scripting
  • You have a good knowledge in Cluster maintenance as well as creation and removal of nodes using tools like Cloudera Manager as well as performance tuning of Hadoop clusters and Hadoop MapReduce routines.
  • Design and implement column family schemas of HBase, apply different HDFS formats and structure like Parquet, Avro, etc. to speed up analytics, Fine tune Hadoop applications for high performance and throughput. Troubleshoot and debug any Hadoop ecosystem run time issues
  • You are a dedicated problem solver with production support experience and an ability to cooperate within a multidisciplinary, global team. You are a self-starter with a strong curiosity for extracting knowledge from data and the ability to elicit technical requirements from a non-technical audience
  • You bring 10+ years of experience in enterprise IT at a global organization with recent 5+ years in Big Data ecosystem engineering

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