The CISO Cybersecurity Operations Platform (CSOP) team is looking to add an engineer to the Analytics and Data Exploitation team. The Platform provides the technology, services and expertise required by IBM’s Cyber Threat Detection and Response teams. We support the Advanced Threat Detection (threat hunting, intelligence, incident response), Vulnerability Detection and Response, Innovation and Remediation, Security Operations Centers and Command Centers teams to deliver enterprise-wide security to one of the world’s most established technology companies. We process tens of billions of events per day, meaning effective analysis and data exploitation practices are critical to our success. This is a technical position within the Analytics and Data Exploitation team whom employ commercial, open source and in-house developed tools to deliver critical cybersecurity services such as event processing, automation, complex analytics and support to digital investigations. This role operates across our development, test, pre-production and production networks to create, maintain and improve our services –an important component of which is fault-finding and the ability to work within complex, dynamic environments.
The right candidate thrives in high-pressure situations and has practical experience working with Big Data technologies –such as Spark, Hadoop and Elasticsearch. The role requires a proven, practical knowledge of container orchestration technologies – specifically Kubernetes and OpenShift. The work will include the design and optimization of container-deployed systems, as well as the day-to-day engineering and administration of the orchestration environment. This includes cluster management, Pod assignment / configuration, application virtual routing, security, container image registry management and optimization of the runtime engines. Wider knowledge of data ingestion, extraction, transformation and loading technologies is important - including Apache Nifi and Kafka. The role is rounded-out by some software development tasks – all related to cyber security. These will involve Java, Python and shell scripting so experience with DevOps methods is highly advantageous. The Platform team employs hybrid cloud hosting and this includes provisioning, administration and management of services within environments spanning IBM Cloud, Amazon Web Services and Microsoft Azure.
Job Duties:
- Contribute to the day-to-day work that supports our critical cybersecurity analysis and data processing workflows
- Design, manage and maintain parts of our container-hosting environments –specifically, the Kubernetes and OpenShift technologies
- Develop and maintain ingest pipelines with technologies like Nifi and Kafka
- Work with end-users to understand their analysis needs and play an active role in implementing what is required to realize them
- Support the team leadership to improve overall exploitation of technologies that best serve our requirements
- Support fault-finding, problem determination and break-fix tasks
- Work as part of a deeply technical, passionate team of engineers to tackle significant IT challenges
Minimum Requirements:
- 3 or more years’ experience in a big data role –such as operational analysis, data engineering, data science or data engineering
- Experience with container environments –such as Kubernetes, OpenShift or Docker Swarm
- Practical experience programming in high-level languages such as Python, Java, Haskell, C / C++, R or similar
- Experience with (or a proven aptitude for) working within a fast-paced environment where the success criteria are defined by external factors. This includes having to change course quickly, based on the evolving needs of a complex and dynamic environment
- Experience with IBM Cloud, AWS, Azure or similar cloud environments
Required Professional and Technical Expertise :
- Practical, applied knowledge of the Elastic / Cloudera suite with proven experience in using big data for practical purposes
- Direct experience with, or demonstrable aptitude for, Nifi / Kafka / Elastic / Hadoop
- Experience applying data science to practical problems: feature extraction, data representation, statistical analysis and visualization
- Experience with machine learning algorithms and techniques
