Senior Fullstack Developer

This is a remote position.

We are looking for a Senior Full Stack Engineer to join the Finance engineering team. This is more than a hands-on development role. We're looking for someone who will elevate the team's engineering practices, mentor fellow engineers, drive technical excellence, and lead by example in building reliable, scalable, and maintainable software.

You'll work on a mission-critical enterprise platform responsible for managing the complete lifecycle of product pricing across 100+ global markets. The platform powers everything from product catalog ingestion and cost modeling to pricing calculations and the distribution of finalized pricing data to downstream commerce and finance systems.

Our technology stack includes a modern React frontend, multiple Spring Boot microservices supporting REST APIs, event-driven integrations, and batch processing, shared domain libraries, and cloud-native infrastructure running on AWS (EKS, RDS, and S3), with integrations across Google BigQuery and Apache Kafka. We follow a trunk-based development model with automated CI/CD pipelines using GitHub Actions, ArgoCD, and progressive environment promotion from Development through Production.

Success in this role goes well beyond delivering features. You'll help shape the team's engineering culture by improving code review quality, defining architectural patterns, reducing technical debt, strengthening testing and observability practices, and helping engineers grow through coaching, technical discussions, and mentorship. We're looking for someone who raises the bar for the entire team—a force multiplier rather than an individual contributor.

AI-assisted software development is a core part of how we build software. We're looking for an engineer who already uses AI coding assistants such as Cursor, Claude Code, GitHub Copilot, Kiro, or similar tools as part of their daily development workflow, not as an occasional productivity aid, but as an integral part of software design, implementation, debugging, testing, documentation, and code exploration.

Beyond personal productivity, you'll help the team adopt effective AI engineering practices by developing reusable prompts, project context, steering files, MCP configurations, and team workflows that improve developer effectiveness while maintaining high standards for code quality, security, and engineering judgment. 

• 5+ years of professional software engineering experience, including 3+ years in a Senior Software Engineer or Technical Lead role.
  
• Expert-level proficiency in Java 17+ (preferably Java 21), Spring Boot, Spring Data JPA, and Hibernate, with strong knowledge of application architecture, concurrency, performance optimization, resiliency, and error-handling patterns.
  
• Strong frontend engineering experience with React, TypeScript, and modern state management libraries such as React Query, Zustand, or equivalent. Able to design scalable frontend architectures, not just implement predefined requirements.
  
• Deep understanding of relational databases, particularly PostgreSQL, including schema design, query optimization, indexing strategies, transaction management, and database migration best practices.
  
• Proven experience designing and evolving production-grade REST APIs, including versioning strategies, authentication and authorization, pagination, validation, error handling, backward compatibility, and API lifecycle management.
  
• Strong software testing mindset with hands-on experience implementing unit, integration, contract, and end-to-end tests, while advocating for maintainable, testable architectures and quality gates within CI/CD pipelines.
  
• Solid experience with modern development workflows, including Git, trunk-based development, pull request reviews, automated CI/CD pipelines, static analysis, and code quality enforcement.
  
• Working knowledge of cloud-native applications and infrastructure, including AWS (EKS/Kubernetes, RDS, S3) or equivalent cloud platforms, with an understanding of containerization, deployment strategies, scalability, networking, and observability.
  
• Experience building and supporting distributed systems, asynchronous messaging, and event-driven architectures (Kafka or equivalent) is highly desirable.
  

• Experience building large-scale batch processing or ETL pipelines using Spring Batch or similar frameworks.
  
• Experience with Apache Kafka and event-driven architectures, including producer/consumer patterns, retries, dead-letter queues (DLQs), and message reliability.
  
• Experience developing enterprise applications that manage large datasets using components such as AG Grid or equivalent data grid libraries.
  
• Experience working with analytical data platforms such as Google BigQuery or similar cloud data warehouses.
  
• Knowledge of Domain-Driven Design (DDD), modular architectures, shared libraries, and reusable platform components.
  
• Experience implementing authentication and authorization using Microsoft Entra ID, MSAL, OAuth2/OIDC, and role-based access control (RBAC).
  
• Experience integrating enterprise systems such as ERP, pricing, inventory, finance, or supply chain platforms.
  
• Experience deploying and operating applications using Kubernetes, GitOps, and tools such as ArgoCD.
  
• Familiarity with modern Java development tools and libraries such as MapStruct, Lombok, and annotation-based code generation.
  
• Experience with Infrastructure as Code using Terraform, AWS CDK, or similar frameworks.
  
• Experience fostering engineering culture through technical communities, engineering guilds, internal tech talks, or mentoring programs.
  
• Experience driving engineering maturity by introducing practices such as architecture decision records (ADRs), design reviews, observability, quality gates, and standardized software development processes.
  
• Hands-on experience extending AI development workflows through Model Context Protocol (MCP) servers, custom AI agents, developer tooling, or automation.
  
• Experience evaluating, selecting, and rolling out AI development tools across engineering organizations, including security assessments, governance, developer enablement, and adoption strategies.
  
• Contributions to open-source projects, technical writing, conference presentations, or other forms of knowledge sharing, particularly related to software engineering or AI-assisted development.