31 August 2020

If you are keen to be in the intersection between big data and cybersecurity, execute big data related cybersecurity projects and be involved in greenfield initiatives within this field, our client wants you!

Mandatory Skill(s)

  • Background in Computer Science/ Computer Engineer/ Data Science and related disciplines;
  • At least 2 years of experience in data analytics and machine learning model development;
  • Has some exposure into cybersecurity or network infrastructure projects;
  • Good understanding of programming languages (e.g. Python, R, Scala, Golang, Java, C++) and ETL frameworks;
  • Experience in using databases and frameworks for big data analytics (e.g. Hadoop, Hive, Spark) and able to write complex SQL queries;
  • Familiar with big data streaming tools, database frameworks and visualizaton tools (e.g. Apache NiFi, Storm, Spark, Hadoop, Tableau);
  • Good writing, communication and presentation skills;
  • Fast learner and ability to work independently;
  • Strong interest in the latest technology and security developments, especially within the cybersecurity field.

Desirable Skill(s)

  • Has understanding in AI, Machine Learning and Deep Learning concepts.


  • Be involved in projects or programs aim at using big data to improve cybersecurity;
  • Monitor advances in big data technologies and identify new tools and frameworks to improve efficiency and fine tune existing data analytics platforms;
  • Develop and review data models for cybersecurity projects;
  • Cleanse and verify suitable data to be use for data analytics projects;
  • Interpret and analyse big data sets using statistical techniques;
  • Identify cybersecurity use cases to develop data analytics rules and data models to delivery the use case outcomes;
  • Conceptualise and manage the development of infrastructure required for the ETL of big data from various sources;
  • Involve in the enhancements of the infrastructure required for real-time and batch data ingestion and processing pipelines for data analytics, machine learning and dashboarding;
  • Work with external stakeholders to develop data analytics systems and validate the accuracy of data models;
  • Perform scripting to streamline and improve processes related to the flow of data and integration with other platforms.
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