Senior DevOps Engineer (Python) at Technostacks
McKinney, TX 75070, USA -
Full Time


Start Date

Immediate

Expiry Date

30 Nov, 25

Salary

0.0

Posted On

31 Aug, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Confluence, Jenkins, Architecture, Design Principles, Docker, Automation, Git, Azure, Infrastructure, Integration, Python, Load, Devops, Ec2, Security, Kubernetes, Microservices, Code

Industry

Information Technology/IT

Description

REQUIRED SKILLS & EXPERIENCE

  • 5+ years of experience as a DevOps Engineer or in a similar role, with a strong emphasis on Python scripting and automation.
  • Python programming skills with a proven ability to develop scalable, maintainable, and efficient automation scripts for cloud infrastructure and DevOps processes.
  • Strong experience with cloud platforms (AWS, GCP, Azure) and their services, including EC2, S3, Lambda, and Kubernetes.
  • CI/CD tool experience (Jenkins, GitLab CI, CircleCI, Travis CI, etc.) with Python integrations to automate testing, deployment, and integration.
  • Expertise in Infrastructure as Code (IaC) with tools like Terraform, CloudFormation, or
  • Ansible, writing Python scripts for configuration and deployment automation.
  • Hands-on experience with Docker and Kubernetes for containerized applications and cloud- native architecture.
  • Familiarity with monitoring tools (e.g., Prometheus, Grafana, ELK Stack, Datadog) and using Python to create customized monitoring solutions.
  • Experience with version control systems like Git and understanding of branching strategies.
  • Knowledge of networking concepts, such as load balancing, DNS, and firewalls, especially in cloud environments.
  • Understanding of security best practices in DevOps, including role-based access, identity management, and data encryption.

PREFERRED QUALIFICATIONS:

  • DevOps certifications (AWS Certified DevOps Engineer, Google Professional DevOps Engineer, etc.) or Python-related certifications.
  • Familiarity with microservices architecture and cloud-native design principles.
  • Experience with serverless technologies (e.g., AWS Lambda, Azure Functions)
  • Understanding of Agile development methodologies and collaborative tools (Jira, Confluence, etc.).
  • Database experience with cloud-hosted databases (SQL/NoSQL) and optimizing their performance in cloud environments
  • Knowledge of GitOps, Helm charts, and using Python for managing application configurations in Git.
Responsibilities
  • Automation and Infrastructure Management: Use Python to automate infrastructure provisioning, application deployment, and monitoring tasks, ensuring continuous improvement of DevOps workflows.
  • CI/CD Pipeline Development: Build and maintain CI/CD pipelines with Python-driven automation, ensuring rapid, reliable software delivery and integration.
  • Cloud Infrastructure: Design, implement, and optimize cloud-based infrastructure on AWS, GCP, or Azure, using Python for automation and scaling infrastructure in a secure and cost- efficient way.
  • Containerization and Orchestration: Work with Docker and Kubernetes for building, deploying, and managing containerized applications. Use Python to automate deployment processes in Kubernetes clusters.
  • Scripting & Tool Development: Develop Python scripts for day-to-day infrastructure management, monitoring, data collection, and log aggregation. Build custom tools and automation scripts for the development and operations teams.
  • Monitoring & Logging: Design and implement systems for monitoring the health and performance of infrastructure using Prometheus, Grafana, and other monitoring tools, and leverage Python to create customized alerts and notifications.
  • Security Best Practices: Implement security practices by automating vulnerability scans, access control, and compliance checks using Python scripts and DevSecOps principles.
  • Collaboration & Troubleshooting: Work with development teams to debug complex issues in production environments, provide expertise on automation and scalability, and identify bottlenecks in systems or workflows.
  • Documentation: Maintain clear and concise documentation for all Python scripts, deployment pipelines, automation processes, and system changes to ensure smooth handoffs across teams.
  • Performance Optimization: Proactively work to enhance system reliability, optimize resource usage, and improve the overall performance of infrastructure through Python-driven analysis and improvements.
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