Senior Kafka Engineer

Senior Kafka Engineer

Deploy, configure, and manage Apache Kafka clusters and AWS MSK environments. Monitor broker health, partitions, replication factors, and consumer lag. Perform capacity planning and cluster scaling activities. Manage Kafka security using SSL, SASL, ACLs, and encryption standards. Troubleshoot producer, consumer, and broker performance issues. Support Kafka Connect, Schema Registry, Cruise Control, and MirrorMaker implementations. Manage cloud infrastructure services including EC2, S3, IAM, VPC, EBS, CloudWatch, CloudTrail and AWS Glue. Support AWS Managed Streaming for Kafka (MSK), EMR, Lambda, and Airflow environments. Implement cloud security best practices and governance controls. Perform infrastructure provisioning and automation using Infrastructure as Code (IaC). Monitor cloud resource utilization and optimize operational costs. Design and maintain CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or similar tools. Automate infrastructure deployment using Terraform, CloudFormation, and Ansible. Manage source control repositories and release processes. Implement monitoring and alerting solutions using Prometheus, Grafana, Splunk, ELK, or CloudWatch. Support containerization technologies such as Docker and Kubernetes. Develop automation scripts using Python, Shell, or Bash. Participate in on-call support rotations and incident management activities. Perform root cause analysis (RCA) and implement preventive measures. Create and maintain operational documentation and standard operating procedures. Ensure compliance with security, audit, and regulatory requirements.

Required Skills: Apache Kafka Administration, KRAFT and MSK, AWS Cloud Services, Linux (RHEL/Rocky Linux), Shell Scripting and Python, Jenkins, Git, Ansible, Terraform, Docker and Kubernetes, Monitoring Tools (Grafana, Prometheus, Splunk), Networking, Security, and High Availability Concepts, Performance Tuning and Capacity Planning

Preferred Qualifications: Bachelor's degree in Computer Science, Information Technology, or related field. Experience with Cloudera CDP, AWS MSK, Airflow, and Spark. AWS, GCP, Kafka, or Kubernetes certifications. Experience supporting large-scale production environments handling petabyte-scale data workloads. Key Achievements Expected: Maintain platform availability above 99.9%. Adopt AI-assisted engineering practices to improve operational efficiency, reduce manual effort, and accelerate troubleshooting and documentation. Automate repetitive operational tasks. Improve cluster performance and resource utilization. Ensure secure, scalable, and reliable data platform operations. Support enterprise data engineering, analytics, and AI/ML workloads efficiently. Minimum years of experience: 8-10 years.