Senior Full-Stack Engineer (Backend + Frontend)

Soda is a company building a Data & AI performance monitoring platform used by top brands like Disney, Ralph Lauren, CBRE, HelloFresh, 2K Games, and Nubank. Founded in 2019, the company commercializes through Soda Cloud and has investors including Point 9 Capital and Eurazeo.

• 5+ years of software development experience at a product company.
• Strong proficiency in Java including REST APIs, database interaction, and asynchronous processing.
• Experience with TypeScript and a modern frontend framework, preferably Angular.
• Solid frontend fundamentals including component architecture, state management, performance optimization, and responsive design.
• Strong SQL skills in backend contexts, including schema design, query optimization, and migrations; familiarity with MySQL and ClickHouse.
• Habitual testing mindset, including e2e tests with tools like Cypress or Playwright.
• Architectural judgment to design APIs from the consumer's perspective.
• DevOps fluency with containers, CI/CD, infrastructure-as-code, and Linux comfort.
• Experience shipping code with AI coding agents such as Claude Code or Cursor integrated in the workflow.
• Ability to work independently within a distributed asynchronous team.
• Fluent English.

Nice to have:

• Experience with multi-tenant SaaS at scale and high-growth startup environments.
• Eye for UI/UX design detail.
• Knowledge of Python for data engineering.
• Experience building LLM-powered agentic workflows or AI-driven automation.
• Background in data quality, data management, data observability, or ML monitoring.

• Build features end-to-end including backend APIs, database changes, and frontend UI, shipping them to production independently.
• Design and implement backend services for a multi-tenant SaaS platform.
• Work with relational databases (MySQL): schema design, query optimization, and data modeling.
• Build clean and fast frontend features.
• Optimize performance across the stack and write tests at unit, integration, and e2e levels.
• Develop agentic features with Large Language Model-powered workflows.
• Improve systems supporting agentic development such as agent instructions and test feedback loops.
• Collaborate directly with product, design, and customers to decide on features.