Senior / Lead DataOps Engineer

Requirement: Degree in IT, Computer Science, Data Analytics or related field 2 to 4 years of experience in Data Engineering, DevOps, or related fields. Proven experience working in a mature, DevOps-enabled environment with well-established cloud practices, demonstrating the ability to operate in a high-performing, agile team. Familiarity with cloud platforms (AWS, AliCloud, GCP) and experience managing infrastructure across public cloud and on-prem environments, particularly with OpenShift Container Platform (OCP). Practical experience using automation tools such as Ansible, Terraform, and CLI tools for provisioning, configuring, and managing infrastructure across hybrid environments. Competence in designing and implementing data ingestion, ETL frameworks, dashboard ecosystems, and data orchestration to ensure seamless data flow and integration for large-scale datasets. Hands-on experience with Linux systems, Object Storage, Spark, and Presto query engines, and the ability to translate functional specifications into design specifications. Working knowledge of CI/CD best practices, with experience in setting up and managing CI/CD pipelines for continuous integration, testing, and deployment. Experience in implementing security measures, including IAM roles, policies, Security Groups, and Network ACLs, to protect data across cloud and on-prem platforms. Familiarity with monitoring and optimization tools such as AWS CloudWatch, Prometheus, and Grafana to help ensure performance and reliability in data systems. Good problem-solving and communication skills, especially in explaining technical concepts to non-technical data users, and the ability to collaborate effectively with data engineers, data stewards, and other stakeholders. Ability to maintain clear documentation of data infrastructure and processes, with some experience in providing training on DataOps practices to team members. Preferred: Certifications in cloud technology platforms (such as cloud architecture, container platforms, systems, and/or network virtualization). Knowledge of telecom networks, including mobile and fixed networks, will be an added advantage. Familiarity with data fabric and data mesh concepts, including their implementation and benefits in distributed data environments, is a bonus. #J-18808-Ljbffr