Senior Full-Stack Engineer (Backend + Frontend)

Soda Data is building a Data & AI performance monitoring platform used by companies like Disney, Ralph Lauren, and Tesla. Founded in 2019, the company combines automated post-production monitoring with pre-production pipeline data testing and offers its solution through Soda Cloud.

• 5+ years building software at a product company with ownership of production systems.
• Strong Java skills: REST APIs, database interactions, async processing.
• Experience with TypeScript and modern frontend frameworks, preferably Angular.
• Solid frontend fundamentals: complex UIs, component architecture, state management, performance, responsive design.
• Strong SQL skills in backend context: schema design, query optimization, migrations in production; experience with MySQL and ClickHouse.
• Habitual testing including end-to-end tests (e.g., Cypress, Playwright).
• Good architectural judgment designing APIs with attention to data flow and latency.
• DevOps fluency with containers, CI/CD, infrastructure-as-code, and Linux comfort.
• Experience shipping production code with AI coding agents (e.g., Claude Code, Cursor).
• Independent work style in a distributed, async team.
• Fluent in English.

Nice to have:

• Multi-tenant SaaS and high-growth startup experience.
• Eye for design details.
• Knowledge of Python for data engineering.
• Experience building LLM-powered agentic workflows and AI-driven automations.
• Background in data quality, data management, data observability, or ML monitoring.

• Build features end-to-end: backend APIs, database changes, frontend UI, and ship to production.
• Design and implement backend services for a multi-tenant SaaS platform.
• Work with relational databases (MySQL): schema design, query optimization, data modeling.
• Build frontend features focusing on performance and usability.
• Optimize performance across the stack and write tests at all layers (unit, integration, e2e).
• Develop agentic features powered by large language models to help users find and fix data issues.
• Improve agentic development systems including agent instructions and test feedback loops.
• Collaborate directly with product, design, and customers to decide development priorities and directions.