Kafka Engineer

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.
AWS Cloud Administration
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.
DevOps & Automation
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.
Operations & Support
Provide Level 2 and Level 3 production support.
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 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, Google Cloud Platform, 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.

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