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Senior & Principal Software Engineers


Genesys is building the data platform of the future with a small team with a startup feel and the financial stability of an industry leader. We’re experiencing hyper growth and scalability is a key focus with hundreds of terabytes of data our customers generate. Our Analytics team is exposed to a large variety of technology with a chance to move around and work on something new practically every day. Working remote in the US or Canada is fine.



As a member of the team, you will:





  • Develop and deploy highly-available, fault-tolerant software that will help drive improvements towards the features, reliability, performance, and efficiency of the Genesys Cloud Analytics platform.
  • Actively review code, mentor, and provide peer feedback.
  • Collaborate with engineering teams to identify and resolve pain points as well as evangelize best practices.
  • Partner with various teams to transform concepts into requirements and requirements into services and tools.
  • Engineer efficient, adaptable and scalable architecture for all stages of data lifecycle (ingest, streaming, structured and unstructured storage, aggregation) in support of a variety of data applications.
  • Build, deploy, maintain, and automate large global deployments in AWS.
  • Troubleshoot production issues and come up with solutions as required.
  • You have a strong engineering background with ability to design software systems from the ground up.
  • You have expertise in Java, Python or similar programming languages.
  • You have experience in web-scale data and large-scale distributed systems on a cloud infrastructure.
  • You have a product mindset. You are energized by building things that will be heavily used.
  • You have engineered scalable software using big data technologies.
  • You have worked on and understand messaging/queueing/stream processing system.
  • You have deep knowledge of Kafka, Flink, or Druid.
  • You design not just with a mind for solving a problem, but also with maintainability, testability, monitorability, and automation as top concerns.
  • Right tool for the right job over we-always-did-it-this-way.
  • We pick the language and frameworks best suited for specific problems. This usually translates to Java for developing services and applications and Python for tooling.
  • Packer and Ansible for immutable machine images.
  • AWS for cloud infrastructure.
  • Infrastructure (and everything, really) as code.
  • Automation for everything. CI/CD, testing, scaling, healing, etc.
  • Apache Flink and Apache Kafka for stream processing.
  • Amazon DynamoDB, Postgres, Amazon S3, and Redis for query and storage.






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