Lead Software Engineer - Full-Stack

Job Description

Shape the infrastructure that empowers teams, unlocks innovation, and fosters continuous growth for yourself and your peers.

Job summary

You will contribute to the technical direction of the team, take responsibility for end-to-end delivery, and help establish engineering standards that advance reliability, efficiency, and maintainability.

• Design, develop, and deploy scalable full-stack platform tools for infrastructure teams.
• Enhance reliability and performance of critical internal systems through engineering best practices.
• Collaborate with engineering and operations groups to understand requirements and deliver solutions.
• Integrate new technologies and frameworks into platform services to improve developer productivity.
• Automate workflows and monitoring processes to streamline operations and reduce manual effort.
• Review code, mentor junior engineers, and contribute to team knowledge sharing.
• Participate in planning, testing, and release processes to ensure robust software delivery.
• Leverages enterprise-authorized AI capabilities within the work environment to accelerate data architecture analysis and decisioning (e.g., option evaluation and documentation), validating outputs and handling data according to sensitivity and security requirements.
• Drives reuse-first adoption of AI-assisted data validation within SDLC/toolchain routines, improving quality checks and operational stability with traceability/auditability and resiliency expectations.

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Professional experience building full-stack applications for infrastructure or platform teams.
• Proficiency in modern programming languages such as JavaScript/TypeScript, Python, Java, or Go.
• Experience developing and deploying microservices and web applications using popular frameworks.
• Strong grasp of database systems (SQL and NoSQL), RESTful APIs, and cloud infrastructure concepts.
• Familiarity with CI/CD pipelines, DevOps practices, and automated testing methodologies.
• Demonstrated ability to work cross-functionally on complex projects and deliver high-quality results.
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data architecture workflows with strong validation habits and awareness of data sensitivity.
• Ability to assess and validate AI-assisted data architecture recommendations before adoption, escalating uncertainty and ensuring outcomes align to resiliency, security, and auditability expectations.

• Experience with container orchestration tools (Kubernetes, Docker) in production environments.
• Knowledge of observability, monitoring, and logging solutions for large-scale systems.
• Background in security compliance or distributed infrastructure design.
• Contributions to open source or active participation in developer communities.