MrQ operates a UK-based online casino with a tech stack heavy on Java, Spring, Kafka, and Kubernetes — infrastructure choices that signal a platform built for high-throughput transactions and real-time data flow rather than traditional casino software. The hiring mix (18 engineers, 4 designers, 4 product roles) and active project list (automated testing frameworks, segmentation engine, wallets integration, gameplay flows) show a company scaling both testing discipline and payment infrastructure simultaneously, while pain points around design-system scaling and global platform deployment reveal the friction points of rapid growth in a regulated vertical.
Notable leadership hires: React Native Lead
MrQ is a UK-headquartered online casino operator founded in 2018, now a public company with 51–200 employees. The platform is anchored on engineering fundamentals: instant payment processing, automated testing at scale, and player segmentation and risk scoring. Core business surfaces include onboarding flows, gameplay mechanics, wallet integration, and a design system that powers web and mobile experiences. The company maintains operations and hiring across the UK, Gibraltar, and Malta. Active pain points center on continuous delivery infrastructure, global scaling, mobile performance optimization, and the structural tension between compliance requirements and commercial velocity.
Java, Spring, Kafka, Redis, Kubernetes, MongoDB, Angular, Kotlin for backend and frontend. Salesforce and HubSpot for CRM. Optimove, Dotdigital, Marketo for marketing automation. Testing via Playwright, Cypress, Cucumber, k6, BrowserStack.
Design system scaling, end-to-end automated test frameworks, segmentation engine, risk scorecards, wallets integration, onboarding and gameplay flows, and a sandbox/rule testing environment for compliance and product iteration.
MrQ's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.