full stack developer in iGaming

The client is a product company in the iGaming sphere.

• Take ideas from zero to one, prototype quickly, ship, and iterate;
• Build and scale high-throughput, low-latency microservices;
• Own the full stack loop, including the data model, API, UI, deployment, and monitoring;
• Make architectural decisions and take responsibility for their outcomes;
• Optimize performance and costs across database and infrastructure layers;
• Collaborate with product, design, and engineering colleagues in a monorepo setup;
• Use modern AI tools to accelerate development and expand what one engineer can ship.

• 5+ Years of experience building software as a full stack developer;
• A strong builder mentality and a track record of shipping software;
• Deep JavaScript and TypeScript expertise across the stack;
• Strong React.js and Next.js skills;
• Node.js backend experience, ideally with Nest.js, in a microservices architecture;
• Comfortable working in a monorepo environment;
• Experience with relational databases such as PostgreSQL, plus interest in or exposure to columnar analytics stores such as ClickHouse;
• Experience building and operating scalable systems under production load;
• Hands-on experience integrating AI coding tools such as Claude Code, Cursor, Codex, and OpenAI models into a real workflow;
• Belief in AI's potential for developers and enthusiasm for building with it;
• Nice to have: Vue.js, experience in fintech or iGaming or other high-stakes and high-throughput domains, hands-on experience with Cloudflare Workers, edge infrastructure, and AWS, a track record of making systems reliable at scale, familiarity with event-driven architectures, CDC pipelines, or real-time data flows, side projects, or open-source contributions.

• Salary of EUR 60,000 - 75,000 gross/year, based on experience;
• Hybrid working arrangement after completion of the 6 months probation period;
• Real ownership and decision-making authority, with no layers of sign-off between the developer and production;
• Daily releases and an AI-forward culture that encourages the use of the best tools;
• Challenging problems involving scale, performance, and real-time data.