Devops Engineer

Location: San Antonio

Devops Engineer

Tools & Technologies:

• Apache Kafka (Self-managed or MSK)
• AWS managed Apache Flink
• Amazon EC2, S3, RDS, and VPC
• Terraform/CloudFormation
• Docker, Kubernetes (EKS)
• Elk, CloudWatch
• Python, Bash

Skills and Expertise:

1. AWS Managed Services:

• Proficiency in AWS services such as Amazon MSK (Managed Streaming for Kafka) , Amazon Kinesis , AWS Lambda , Amazon S3 , Amazon EC2 , Amazon RDS , Amazon VPC , and AWS IAM .
• Ability to manage infrastructure as code with AWS CloudFormation or Terraform .

1. Apache Flink:

• Understanding of Apache Flink for real-time stream processing and batch data processing.
• Familiarity with Flinks integration with Kafka , or other messaging services.
• Experience in managing Flink clusters on AWS (using EC2, EKS, or managed services).

1. Kafka Broker (Apache Kafka):

• Deep knowledge of Kafka architecture , including brokers, topics, partitions, producers, consumers, and zookeeper.
• Proficiency with Kafka management , monitoring, scaling, and optimization.
• Hands-on experience with Amazon MSK (Managed Streaming for Kafka) or self-managed Kafka clusters on EC2.

1. DevOps & Automation:

• Strong experience in automating deployments and infrastructure provisioning.
• Familiarity with CI/CD pipelines using tools like Jenkins , GitLab , GitHub Actions , CircleCI , etc.
• Experience with Docker and Kubernetes , especially for containerizing and orchestrating applications in cloud environments.

1. Programming & Scripting:

• Strong scripting skills in Python , Bash , or Go for automation tasks.
• Ability to write and maintain code for integrating data pipelines with Kafka , Flink , and other data sources.

1. Monitoring & Performance Tuning:

• Knowledge of CloudWatch , Prometheus , Grafana , or similar monitoring tools to observe Kafka, Flink, and AWS service health.
• Expertise in optimizing real-time data pipelines for scalability, fault tolerance, and performance.

Responsibilities:

1. Infrastructure Design & Implementation:

• Design and deploy scalable and fault-tolerant real-time data processing pipelines using Apache Flink and Kafka on AWS.
• Build highly available, resilient infrastructure for data streaming, including Kafka brokers and Flink clusters.

1. Platform Management:

• Manage and optimize the performance and scaling of Kafka clusters (using MSK or self-managed).
• Configure, monitor, and troubleshoot Flink jobs on AWS infrastructure.
• Oversee the deployment of data processing workloads, ensuring low-latency, high-throughput processing.

1. Automation & CI/CD:

• Automate infrastructure provisioning, deployment, and monitoring using Terraform , CloudFormation , or other tools.
• Integrate new applications and services into CI/CD pipelines for real-time processing.

1. Collaboration with Data Engineering Teams:

• Work closely with Data Engineers , Data Scientists , and DevOps teams to ensure smooth integration of data systems and services.
• Ensure the data platforms scalability and performance meet the needs of real-time applications.

1. Security and Compliance:

• Implement proper security mechanisms for Kafka and Flink clusters (e.g., encryption, access control, VPC configurations).
• Ensure compliance with organizational and regulatory standards, such as GDPR or HIPAA, where necessary.

1. Optimization & Troubleshooting:

• Optimize Kafka and Flink deployments for performance, latency, and resource utilization.
• Troubleshoot issues related to Kafka message delivery, Flink job failures, or AWS service outages.