What we’re looking for…

ScienceLogic is looking for a Software Engineer in Test to build automated test scripts for testing web interfaces, API and database services to ensure the quality of our software performance under various conditions.  This role provides a unique opportunity to join a robust software engineering team and contribute to the development and expansion of automated testing within the organization, especially for our AI/ML hybrid cloud monitoring platform, SL1.  

Who we are…

We are a Forrester Wave Leader.  We are a small, nimble software company who is reinventing the way companies manage their hybrid cloud data and systems.  Through AI/ML, we provide modern IT Operations with actionable insights to predict and resolve problems faster and smarter.  Our Engineers and Product Managers are some of the brightest, most creative minds in their field and we are passionate about our product.  We work hard and play hard.  We use bleeding edge technologies on our industry leading SL1 AIOps platform.

What you’ll be doing…

  • Learn to define effective test cases to include edge cases and negative tests
  • Code and run automated tests for our Integration Service application (various types from end-to-end to unit tests to improve missing coverage)
  • Work with engineers to define and build automated tests
  • Learn to diagnose failed Continuous Integration (CI) tests
  • Understand the trade-off of test value against resource and time usage of automated tests
  • Under minimal direction, contribute to building out an extensive automated test infrastructure, including defining the testing scope, that will eventually span the entire product stack.
  • Using modern tools and your own technical knowledge, assist the team in finding the best, most modular way to create test automation.
  • Work in a collaborative and agile environment that is geared towards a Continuous Delivery mechanism, exhibiting strong time management and communication skills; as well as flexibility/agility to changing environments.
  • Actively contribute in Scrum standups, backlog grooming, reviews and retrospectives.

Qualities you possess…

  • Bachelor’s degree in CS or an equivalent technical discipline and 3+ years’ experience as a Software Engineer in Test.
  • 2+ year’s practical QA experience with proficiency in both manual and automation testing.
  • Intermediate level of proficiency with Python, JavaScript or golang, must be able to troubleshoot defects and write basic code for automation
  • Intermediate level of proficiency with MySQL or NoSQL Databases like Couchbase, must be able to develop and execute SQL queries to validate functionality and perform data validation.
  • Can demonstrate an intermediate level understanding of Linux, must be able to understand intermediate level command lines for troubleshooting purposes
  • Has demonstrated success working in high-volume data testing environments is a plus
  • Has demonstrated success using automation tools such as Selenium or similar
  • Has a good understanding of container based technologies like Docker, Swarm, Kubernetes, Rancher, Helm charts
  • Working knowledge of Behavior Driven Development with experience in Cucumber or other Gherkin syntax based frameworks like Behave or Lettuce
  • Working knowledge of continuous integration tools such as Jenkins or Travis
  • Previous experience working in an Agile environment, including working knowledge of basic Agile principals and ceremonies
  • Demonstrated success in utilizing Test Driven Development approaches
  • Working knowledge of scale testing using tools such as Apache JMeter, Silk Performer or HP LoadRunner is a plus
  • Experience with testing queuing systems like Kafka, RabbitMQ, WebsphereMQ is a plus
  • Candidate must possess the ability to produce top quality deliverables from start to finish
  • Self-starter who enjoys problem solving through innovation and collaboration

About ScienceLogic

ScienceLogic is a leader in IT Operations Management, providing modern IT operations with actionable insights to predict and resolve problems faster in a digital, ephemeral world. Its solution sees everything across cloud and distributed architectures, contextualizes data through relationship mapping, and acts on this insight through integration and automation.


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