Software Engineer/Developer, Senior

As a Senior Software Engineer/Developer at AMERICAN SYSTEMS, your main responsibilities include serving as the back-end developer for in-house applications, focusing on system integration and automation. You'll engage in full-cycle software development while collaborating with team members and application architects to ensure seamless integration across systems. Writing code in both Perl and Python and developing automation tools and scripts are also critical components of your role.

To qualify for the Senior Software Engineer/Developer position at AMERICAN SYSTEMS, candidates need to be U.S. citizens with a current Secret clearance and possess a Security+ certification or be able to obtain one within the first four months. A minimum of 3-6 years of software development experience, including at least 2 years of experience in converting Perl to Python and using UNIX-like command lines, is required.

Candidates for the Senior Software Engineer/Developer position at AMERICAN SYSTEMS should be proficient in programming with Python (3+ years), have experience with web frameworks such as Flask, and be familiar with version control systems. Additionally, competence in using Docker and UNIX-like command lines is essential for success in this role.

The salary range for the Senior Software Engineer/Developer at AMERICAN SYSTEMS is between USD $80,100.00 and USD $133,700.00 per year. Actual compensation will depend on various factors, ensuring a fair approach to pay transparency within the organization.

At AMERICAN SYSTEMS, employees enjoy a comprehensive benefits program that includes healthcare benefits, paid leave, retirement plans, insurance programs, and educational and training assistance. These benefits support the welfare of employees and their dependents, reflecting the company's commitment to their well-being.

When converting code from Perl to Python, I begin by analyzing the core functionality of the Perl script. I carefully map out the logic and identify equivalent Python libraries and functions. Emphasizing readability and maintaining performance, I ensure that the Python code is optimized for efficiency and adheres to best practices.

In my previous roles, I've integrated various APIs to enhance application functionality. I usually start with thorough documentation, ensuring I understand the API endpoints and data structures. Implementing RESTful principles, I develop efficient requests using libraries like 'requests' in Python and ensure robust error handling to maintain application stability.

I am well-versed in Scrum and Kanban methodologies. In previous projects, I participated in daily stand-ups and sprint planning sessions, focusing on prioritizing tasks and maintaining stakeholder engagement. This iterative approach allowed me to adapt quickly to changing project requirements and continuously deliver value.

In a recent project, I worked on integrating a complex legacy system with modern cloud services. The challenge was to maintain data integrity while ensuring minimal downtime. I implemented a phased approach, utilizing comprehensive testing and rollback plans to ensure a smooth transition, resulting in successful integration and system performance enhancement.

My debugging strategy typically starts with analyzing logs and identifying error patterns. I prioritize unit testing for individual components and then integrate various levels of testing, such as integration and end-to-end tests. Utilizing tools such as pytest or unittest in Python has helped me maintain high code quality and reliability.

Effective collaboration is key in software development. I ensure transparent communication through regular meetings, utilizing tools like JIRA for task management. By sharing my progress and soliciting feedback early in the development cycle, I enhance team synergy and ensure alignment with architectural guidelines.

I have experience creating automated deployment scripts using tools like Ansible and Docker. These automations have significantly reduced manual intervention, streamlined deployments, and minimized errors. This allows for more consistent environments and frees up time for other development tasks.

Certainly! When I had to familiarize myself with Kubernetes, I dedicated time to understand its architecture and best practices by following online courses and documentation. Implementing it in a deployment scenario enhanced my understanding and ultimately improved our application's scalability and reliability.

Infrastructure as Code (IaC) is an essential practice for automating the management of infrastructure. By defining infrastructure setup in code, it allows us to easily replicate environments and maintain consistency. I’ve utilized tools like Terraform to implement IaC principles effectively.

I actively participate in online communities, attend webinars, and follow numerous tech blogs and podcasts. Platforms like GitHub and Stack Overflow are invaluable resources for both collaborative knowledge sharing and staying abreast of evolving technologies and frameworks common in the industry.