Staff Software Engineer - Full Stack

Tailored Brands•Dublin, CA

1d•Hybrid

About The Position

Tailored Brands is a leading omnichannel specialty retailer of menswear, including suits, formalwear and a broad selection of business casual offerings. The company aims to help its customers love the way they look and feel for their most important moments by delivering personalized products and services through a convenient network of stores and e-commerce sites. The company’s brands include Men’s Wearhouse, Jos. A. Bank, Moores and K&G Fashion. At Tailored Brands, we value differences and know they make us better. We are always working to make our Company a more diverse, equitable and inclusive (DEI) place to work. We currently have exciting opportunities for Full Stack Staff Software Engineers to join our Digital Engineering team. In this role, you'll play a key part in architecting and developing high-performance, scalable web applications across both front-end and back-end systems.

Requirements

Bachelor’s degree or greater in computer science or equivalent.
Overall 12+ years of applicable experience.
Java: 8+ years of experience building scalable, enterprise-level back-end systems using Java (e.g., Spring Boot).
Node.js: 4+ years of experience developing server-side applications with Node.js, including experience with RESTful APIs and GraphQL.
Databases: Hands-on experience with relational (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB) databases, including schema design, query optimization, and transactions.
Cloud & DevOps: Familiarity with cloud platforms (AWS, Azure, GCP) and containerization tools (e.g., Docker, Kubernetes).
API Design: Expertise in designing and consuming RESTful APIs, working with third-party APIs, and ensuring secure and efficient data flow across services.
Unit Testing: Experience with automated testing frameworks (e.g., JUnit, Mocha, PyTest) and a strong focus on test-driven development (TDD) for both back-end and front-end code.
Version Control: Proficiency in Git, including branching, pull requests, and managing large codebases.
Agile Methodologies: Experience working in Agile teams using Scrum or Kanban methodologies.
Leadership Experience: Prior experience leading or mentoring a team of engineers, contributing to technical strategy, and driving initiatives to improve engineering practices.
Problem-Solving & Leadership: Strong analytical skills with a passion for solving complex technical problems. Proven ability to take ownership of projects and provide technical leadership and mentorship to team members.
Communication & Collaboration: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders. A collaborative mindset with the ability to work effectively across cross-functional teams.

Responsibilities

Architect, design, and develop scalable and performant full-stack web applications, leading the development of both front-end (React/Next.js) and back-end (Java/Node.js) components.
Build and optimize Java-based back-end systems, including microservices, APIs, and databases. Ensure systems are scalable, performant, and maintainable.
Design and implement efficient, event-driven, and scalable services using Node.js, ensuring high availability and performance.
Work closely with product managers, designers, QA engineers, and other stakeholders to define requirements and deliver high-quality software solutions.
Provide technical leadership and mentorship to junior engineers, guiding them in best practices, code quality, and system design.
Conduct peer code reviews, establish coding standards, and ensure best practices for software development, including testing and deployment.
Focus on optimizing performance, including database queries, and addressing scaling issues to handle increasing load and traffic efficiently.
Participate in Agile development processes, including sprint planning, retrospectives, and contribute to continuous improvements in engineering practices.
Apply large language models (LLMs) to enrich product catalog data—automating attribute generation, validation, and enhancement to improve discovery, personalization, and SEO.
Build scalable AI-driven pipelines and APIs that embed LLM enrichment into catalog systems for automated classification and contextual tagging.