Senior Software Engineer II

Role Overview

Architecture and Engineering

• Contribute to component-level and service-level designs aligned with established architecture.
• Create technical design documents for medium-to-large features.
• Evaluate dependencies, technical impacts, trade-offs, and implementation approaches.
• Translate business requirements into scalable engineering solutions.
• Design, build, and maintain Python backend services and automation frameworks.
• Design APIs, data models, and integration workflows.
• Refactor existing solutions to reduce technical debt and improve maintainability.

Collaboration, Security, and Delivery

• Review pull requests for quality, readability, test coverage, and standards adherence.
• Mentor team members while contributing to coding and technical design.
• Collaborate with product, QA, DevOps, and platform teams.
• Apply secure coding practices and address identified vulnerabilities.
• Integrate security checks into CI/CD workflows and automation pipelines.
• Refine user stories and technical tasks with product management.
• Coordinate dependencies and support frequent feature delivery and deployment.

Required Qualifications

• 7+ years of software engineering experience is required.
• 4+ years of hands-on production Python development is required.
• Experience designing APIs, microservices, and distributed systems is required.
• Experience with AWS, Azure, or Google Cloud is required.
• Experience implementing CI/CD pipelines using GitHub Actions, Jenkins, or similar tools is required.
• Experience with Terraform, CloudFormation, or similar Infrastructure as Code tools is required.
• Experience with Docker and Kubernetes is required.
• Experience mentoring engineers or leading feature teams is required.
• Developer tooling or automation platform experience is required.
• DevSecOps, security automation, networking, and modern application architecture knowledge are required.

Preferred Qualifications

• Enterprise-scale distributed environment experience is preferred.
• Familiarity with AI/ML or LLM-based automation solutions is preferred.
• Exposure to observability tools such as Splunk, Datadog, or CloudWatch is preferred.