Senior Software Engineer

Description

The Development Enablement team is a new engineering group focused on streamlining the development experience, providing guidance, and addressing challenges in working with the technology stacks maintained by the departmental Architecture team. A Senior Software Engineer for this group will provide technical guidance and assistance to product engineers, identify and resolve development challenges, and champion best practices and technology adoption.

The ideal candidate is a technical leader responsible for setting best practices, establishing development standards, supporting/implementing system design decisions that impact team and organizational success, and identifies opportunities to leverage AI to improve development workflows. This role combines advanced technical expertise with technical leadership responsibilities, focusing on mentoring engineers, defining technical roadmaps, and ensuring delivery of high-quality, secure, and scalable software solutions.

Responsibilities

• Define and establish technical standards from architectural guidelines

• Build scalable, maintainable software systems

• Drive adoption of emerging technologies, selected by leads

• Mentor junior and mid-level engineers

• Participate in hiring processes and technical interviews

• Collaborate with leadership on technical roadmaps

• Represent technical perspectives in strategic planning

• Establish code quality standards and technical debt management within the team

• Create technical documentation and knowledge-sharing resources

• Collaborate with cross-functional teams

• Identify opportunities to leverage Agentic AI to improve developer productivity, streamline engineering workflows, and enhance software quality

• Guide engineering teams in the responsible adoption of AI-assisted development practices and tooling

• Evaluate and implement AI-driven solutions that support development enablement, automation, and operational efficiency

• Establish best practices for integrating Agentic AI into the software development lifecycle

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or related technical field, OR equivalent professional experience demonstrating expert-level programming competency and proven technical leadership capability
• Typically 5+ years of professional software development experience with demonstrated progression to technical leadership roles
• Proven track record of leading complex technical projects
• Experience with large-scale system design and production system management
• History of contributing to technical decision-making and establishing best practices
• Technologies: Proficiency with C#, Angular, SQL, Linux, git, Docker, and AWS; or similar
• Advanced Programming: Expert-level proficiency in multiple languages with deep ecosystem knowledge
• System Architecture: Extensive experience with scalable, distributed systems and cloud-native patterns
• DevOps & Infrastructure: Advanced CI/CD, containerization, orchestration, infrastructure as code
• Performance & Scale: Application tuning, database optimization, caching, system scaling
• Security & Quality: Deep understanding of secure development practices and quality assurance methodologies
• Emerging Technologies: Active knowledge of industry trends and innovative practices
• Technical Leadership: Proven ability to influence technical decisions and establish technical vision
• Mentoring Excellence: Strong coaching skills with track record of developing engineers
• Excellent Communication: Exceptional skills for technical documentation, presentations, cross-functional collaboration
• Strategic Thinking: Balance technical considerations with business objectives
• Change Leadership: Experience driving organizational change and technology adoption
• Advanced Problem Solving: Complex technical and organizational challenge resolution
• Agentic AI: Experience leveraging and creating AI-assisted development tools and agentic workflows to improve engineering efficiency, accelerate delivery, and support software quality initiatives
• AI Governance & Enablement: Understanding of responsible AI adoption practices, including security, compliance, data privacy, and human oversight within engineering workflows