DevOps Engineer - IBM/USAA
Location: San Antonio, TX
Tools & Technologies:
• Apache Kafka (Self-managed or MSK)
• AWS Managed Apache Flink
• Amazon EC2, S3, RDS, and VPC
• Terraform/CloudFormation
• Docker, Kubernetes (EKS)
• ELK Stack, CloudWatch
• Python, Bash
Skills & Expertise:
1. AWS Managed Services:
◦ Proficiency in AWS services like Amazon MSK, Kinesis, Lambda, S3, EC2, RDS, VPC, and IAM.
◦ Experience with infrastructure management using Terraform or AWS CloudFormation.
2. Apache Flink:
◦ Understanding of real-time stream and batch data processing with Apache Flink.
◦ Familiarity with Flink's integration with Kafka and managing Flink clusters on AWS.
3. Kafka Broker (Apache Kafka):
◦ Strong knowledge of Kafka architecture, brokers, topics, partitions, producers, and consumers.
◦ Experience with Kafka management, monitoring, scaling, and optimization, particularly with Amazon MSK or self-managed Kafka clusters.
4. DevOps & Automation:
◦ Expertise in automating deployments and infrastructure provisioning.
◦ Familiarity with CI/CD pipelines using tools like Jenkins, GitLab, GitHub Actions, CircleCI.
◦ Experience with Docker and Kubernetes for containerizing and orchestrating applications in the cloud.
5. Programming & Scripting:
◦ Strong scripting skills in Python, Bash, or Go for automation tasks and data pipeline integrations.
6. Monitoring & Performance Tuning:
◦ Proficiency with monitoring tools like CloudWatch, Prometheus, Grafana.
◦ Expertise in optimizing data pipelines for scalability, fault tolerance, and performance.
Responsibilities:
1. Infrastructure Design & Implementation:
◦ Design and deploy scalable, fault-tolerant real-time data processing pipelines using Apache Flink and Kafka on AWS.
2. Platform Management:
◦ Manage and optimize Kafka clusters (MSK or self-managed) and Flink jobs on AWS infrastructure for low-latency, high-throughput processing.
3. Automation & CI/CD:
◦ Automate infrastructure provisioning and deployment using Terraform, CloudFormation, or other tools.
◦ Integrate new applications into CI/CD pipelines for real-time data processing.
4. Collaboration with Data Engineering Teams:
◦ Work with Data Engineers, Data Scientists, and DevOps teams to ensure smooth integration and high performance of real-time data systems.
5. Security and Compliance:
◦ Implement security for Kafka and Flink clusters, ensuring compliance with regulatory standards (e.g., GDPR, HIPAA).
6. Optimization & Troubleshooting:
◦ Optimize Kafka and Flink performance, troubleshoot issues related to message delivery, job failures, or AWS service outages.
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