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Systems Engineer - Life Science Solutions

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IT/Technical & Product Development
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Elsevier
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TEC0017S Requisition #

 

Elsevier is a global information analytics company that helps institutions and professionals progress science, advance healthcare and improve performance for the benefit of humanity.

 

We are in the midst of a transformation that is being driven by digital technology. Our innovative technology platforms and smart content solutions operate at the cutting edge of big data, semantic web and cloud technology, enabling faster more effective critical decision-making daily across the globe.

 

This is a unique opportunity to join Elsevier Life Sciences Solutions team as a Systems Engineer working on products that significantly contribute to the areas of Pharmacovigilance and Drug Safety making a real impact on quality of life and patients’ wellbeing.

 
The Role

We are looking for an engineer to join our systems engineering team inside Infrastructure and Operations to assist in the design, build and maintenance of application stacks for product teams working in this space.

 
Responsibilities will include:
  • Designing and building infrastructure in AWS as part of a product team
  • Helping the product team support the application in production from a systems’ point of view
  • Supporting developers with core tools (Jenkins, git etc)
  • Optimising costs in AWS
  • Ensuring Elsevier TIO (Technology, Infrastructure and Operations) standards of operational maturity are maintained inside the product’s infrastructure
  • Building tools to help other team members self-serve for most operational tasks
 
Key Skills and Experience
  • Supporting product teams with customer facing products
  • AWS infrastructure
  • Excellent linux experience
  • Infrastructure as code and configuration management (ideally terraform and puppet/ansible)
  • Agile experience working in a scrum team
  • Ability to work in a team with people from all backgrounds
  • Several of the following skills:
    • Experience running and managing ElasticSearch
    • Linux experience at scale
    • Scripting (bash, python, perl, ruby or another scripting language)
    • Large scale monitoring and reporting (New Relic)
    • Continuous integration systems (git/Jenkins)
    • Kubernetes or other container orchestration systems

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