Data Ops. Engineer for A fast growing Big Data team in a financial institution JB-2003

In this role you will manage an analytics platform comprised of mainly Machine Learning (ML) algorithms both in development and in production. The platform work with different Databases and distributed data systems. In your job you will work closely with ML engineers and provide them with solutions to a wide range of needs and develop advanced CICD pipelines both for batch and API processes. Solid technical understanding in distributed data systems and large scale processes is required

Requirements

  • Strong coding abilities in Python and experienced in code review
  • DevOps methodologies (CI/CD)
  • Data pipelines – design and build data infrastructure for data extraction, preparation, and loading of data from a variety of sources
  • Automation, quality checks and monitoring processes
  • Solid infrastructure view
  • Serving methodology
  • Understating of machine learning and data science Methodologies – big advantage
  • 3+ years of developing experience with Python
  • 2+ years of experience with Shell scripts
  • 3+ years of experience with Git/Gitlab/Bitbucket and Jenkins
  • 2+ years of coding with SQL and query optimization in MS-SQL/MySQL/Teradata etc.
  • 2+ year experience with Hadoop ecosystem components: HDFS, Hive, Spark, Kafka
  • Working experience with NoSQL database such as Hbase, mongoDB or Cassandra.
  • Working experience with containers and Kubernetes, openShift