Senior Data Engineer

Short Description

Genesys is seeking a Senior Data Engineer who can collaborate with engineering teams to identify and resolve pain points as well as evangelize best practices.

Job Description

Summary:
  • 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, 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 or similar programming languages.
  • 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.
  • You have engineered scalable software using big data technologies (e.g. Hadoop, Spark, Hive, Presto, Flink, Samza, Storm, Elasticsearch, Druid, Cassandra, 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. 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.
  • Flink and Kafka for stream processing.
  • Hadoop, Hive, and Spark for batch.
  • Airflow for orchestration.
  • Druid, Dynamo, Elasticsearch, Presto, and S3 for query and storage.

Senior Data Engineer
Mid-Senior-level Communications | Information | Technology | Information Technology Full-time Engineering | Other | Information Technology Data Engineer
Genesys powers 25 billion of the world's best customer experiences each year. Our success comes from connecting employee and customer conversations on any channel, every day. Over 10,000 companies in 100+ countries trust our #1 customer experience platform to drive great business outcomes and create lasting relationships. Combining the best of technology and human ingenuity, we build solutions that mirror natural communication and work the way you think. Our industry-leading solutions foster true omni channel engagement, performing equally well across all channels, on-premise and in the cloud. Experience communication as it should be: fluid, instinctive and profoundly empowering. Visit genesys.com.
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