Lead Java/Big Data Software Developer


:


  • 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 and proselytize 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, search, aggregation) in support of a variety of data applications.

  • Build abstractions and re-usable developer tooling to allow other engineers to quickly build streaming/batch self-service pipelines.

  • Build, deploy, maintain, and automate large global deployments in AWS.

  • Troubleshoot production issues and come up with solutions as required.


This may be the perfect job for you if:



  • You have a strong engineering background with ability to design software systems from the ground up.

  • You have expertise in Java. Python and other object-oriented languages are a plus.

  • You have experience in web-scale data and large-scale distributed systems, ideally on cloud infrastructure.

  • You have a product mindset. You are energized by building things that will be heavily used.



  • Open to mentoring and collaborating with junior members of the team.

  • Be adaptable and open to exploring new technologies and prototyping solutions within a reasonable cadence.



  • You have engineered scalable software using big data technologies (e.g., Hadoop, Spark, Hive, Presto, Elasticsearch, etc).

  • You have experience building data pipelines (real-time or batch) on large complex datasets.

  • You have worked on and understand messaging/queueing/stream processing systems.

  • You design not just with a mind for solving a problem, but also with maintainability, testability, monitorability, and automation as top concerns.


Technologies we use and practices we hold dear:



  • Right tool for the right job over we-always-did-it-this-way.

  • We pick the language and frameworks best suited for specific problems.

  • Packer and Ansible for immutable machine images.

  • AWS for cloud infrastructure.

  • Automation for everything. CI/CD, testing, scaling, healing, etc.

  • Hadoop, Hive, and Spark for batch.

  • Airflow for orchestration.

  • Dynamo, Elasticsearch, Presto, and S3 for query and storage.


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