Big Data Engineer
Merck Sharp & Dohme Corp.
Prague, Czech Republic
před 61 dny


MSD is a global health care leader with a diversified portfolio of prescription medicines, vaccines and animal health products.

The difference between potential and achievement lies in the spark that fuels innovation and inventiveness; this is the space where MSD has codified its 125-

year legacy. MSD’s success is backed by ethical integrity, forward momentum, and an inspiring mission to achieve new milestones in global healthcare.

MSD’s Information Technology division partners with colleagues across the business to help serve our patients and customers around the world.

Ours is a high-energy team of dynamic, innovative individuals dedicated to advancing MSD’s contribution to global medical innovation by leveraging information and technology to efficiently advance the business by driving revenue and productivity.

As a part of Big Data Platform team you will contribute to MSD Big Data Lake and cooperate with other teams to tackle biggest opportunities at the intersection of healthcare, information and technology.

Be in charge of development, deployment and maintenance of Big Data clusters. Apply best practices, execute releases, provide infrastructure as-

a-code, monitor Hadoop clusters.

We are searching for person in a full-time position willing to adopt new technologies and interested in automation and continuous delivery excellence.

You will be working with an international team located in Prague office FIVE.

Responsibilities :

  • Responsible for implementation and ongoing administration of Hadoop infrastructure
  • Cluster maintenance including creation and removal of nodes
  • Performance tuning of Hadoop clusters
  • Monitoring of Hadoop cluster connectivity and security
  • Hadoop services support and maintenance - HDFS, Hive, HBase and Kafka
  • Software patches and upgrades
  • Automation of manual tasks using Ansible
  • Collaborating with application teams to install operating system and Hadoop updates, patches and version upgrades
  • Deployment of Hadoop cluster, add and remove nodes, keep track of jobs, monitor critical parts of the cluster, configure high availability
  • Research and recommend technical and operational improvements for improved reliability and efficiencies
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