Lead SOC Engineer (Devops)

Overview:

The SOC Lead Engineer, DevOps is a specialized technical role responsible for managing and optimizing large scale platform and data operations within the Security Operations Center (SOC). This individual is proficient in a wide array of big data and DevOps/Platform related tools and technologies. This position requires at least 8 years of experience in DevOps, Big data environments and a solid understanding of Operating Systems, APIs, (ETL) processes, and scripting.

Responsibilities:

Key Responsibilities

• Manage and optimize large-scale data operations and big data technologies.

• Develop scripts and leverage automation tools to streamline operations and minimize manual intervention. This includes automating deployment processes, system configurations, and routine maintenance tasks.

• Involve in architectural discussions and provide the best solution following standard design patterns.

• Provide expert support for system management , monitoring, and troubleshooting. Identify, diagnose, and resolve system issues to minimize downtime.

• Develop and maintain ETL/ELT processes , standards, and procedures for data management to ensure data accuracy and integrity.

• Continuously monitor, manage, optimize, and report on the performance of SOC solutions, identifying and resolving any issues or bottlenecks, incidents, and resolutions.

• Develop, implement, and maintain strategies for data collection , analysis, data processing, and data security using tools like Flink, Spark, CRIBL, Hadoop, ELK, Cloudera, and Data Lake.

• Ensure the optimized operations of big data tools within the SOC.

• Collaborate with different teams to understand data requirements and ensure data availability and quality.

• Participate in continuous process improvements to increase SOC efficiency and effectiveness.

• Contribute to SOC strategy and initiatives related to big data , platform and devops engineering and management.

Qualifications:

Job Specifications

Skills/Certifications (Technical & Non-Technical)

Skills

• Solid understanding of hosted and cloud platforms (AWS, Azure, Google Cloud) and their services.

• Proficiency in working with various Operating Systems (OS) and APIs.

• Proficiency in big data technologies and distributed compute systems such as ELK, CRIBL, Apache Kafka, Data Lake House, and Cloudera.

• Experience with microservices architecture and containerization technologies (e.g., Docker, Kubernetes).

• Extensive knowledge in Git and automated build and deployment tools (such as Jenkins, Azure DevOps Pipelines) and utilizing Ansible, Terraform, etc. in hybrid environments.

• Knowledge of Gitops process and experience with tools like Argo CD, Flux CD etc.

• Strong programming skills, particularly in Python and Bash scripting, for automating data processes and troubleshooting/debugging.

• Extensive knowledge of data management and security principles, including data processing, normalization, data quality, data encryption and database management.

• Good to have an understanding or hands-on experience with ETL/ELT processes, Big Data tools and Data Lake.

• Proficiency in SQL and NoSQL for data manipulation and retrieval.

• Excellent problem-solving skills, with the ability to make decisions under pressure.

• Stakeholder management and experience in building high performing teams.

• High proficiency in written and verbal communication.

• Exceptional collaboration and team development skills including capacity planning.

Certifications

• Cloud-related certifications like AWS Certified Solutions Architect, Google Professional Cloud Architect, or Microsoft Certified: Azure Solutions Architect Expert.

• Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD) would be beneficial.

• Cisco CCNA R&S

• CompTIA Security+ , LPIC certifications or similar would be beneficial.

Minimum Work Experience

• At least 8 years of experience in big data and platform engineering in a cybersecurity context.

• Considerable experience working with a range of big data technologies and tools, cloud environments, and practical knowledge of applying best practices of data security

Education

• Bachelor’s degree in computer science, Information Technology, Cybersecurity, or related field.

• Master's degree in data science or related is a bonus.