Job Title: DevOps Engineer (50% Kafka, 50% DevOps)
Location: San Antonio, USAA
Job Summary:
The DevOps Engineer will work with both Apache Kafka and DevOps tools to design, deploy, and manage real-time data streaming solutions, particularly using AWS services and Apache Flink. This role requires a deep understanding of Kafka, cloud infrastructure, and automation tools.
Key Skills & Expertise:
1. AWS Managed Services:
◦ Proficiency with AWS services such as MSK (Managed Streaming for Kafka), Kinesis, Lambda, EC2, RDS, VPC, IAM, and more.
◦ Ability to manage infrastructure using CloudFormation or Terraform.
2. Apache Flink:
◦ Expertise in Apache Flink for stream and batch data processing, including its integration with Kafka.
◦ Experience with managing Flink clusters on AWS (EC2, EKS, or managed services).
3. Apache Kafka:
◦ In-depth knowledge of Kafka architecture, including brokers, topics, partitions, producers, and consumers.
◦ Experience with managing Kafka clusters (MSK or self-managed).
4. DevOps & Automation:
◦ Experience with automating deployments and infrastructure using CI/CD tools (e.g., Jenkins, GitLab, GitHub Actions).
◦ Proficiency with Docker, Kubernetes, and cloud-based containerization.
5. Programming & Scripting:
◦ Strong scripting skills in Python, Bash, or Go for automation and integration tasks.
6. Monitoring & Performance Tuning:
◦ Knowledge of monitoring tools like CloudWatch, Prometheus, and Grafana.
◦ Expertise in optimizing data pipelines for performance and scalability.
Key Responsibilities:
1. Infrastructure Design & Implementation:
◦ Design and deploy real-time data pipelines using Apache Flink and Kafka on AWS, ensuring scalability and fault tolerance.
2. Platform Management:
◦ Manage and optimize Kafka clusters and Flink jobs for performance and scaling on AWS.
3. Automation & CI/CD:
◦ Automate infrastructure provisioning and monitoring using Terraform, CloudFormation, and other tools.
4. Collaboration:
◦ Work closely with Data Engineers, Data Scientists, and DevOps teams for smooth integration of data systems.
5. Security & Compliance:
◦ Implement security for Kafka and Flink clusters, including encryption and access controls.
6. Optimization & Troubleshooting:
◦ Optimize Kafka and Flink deployments for performance, troubleshoot issues, and ensure reliability
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