About us
We are a product R&D company that creates solutions for the dynamic iGaming ecosystem. Our mission is to build cutting-edge platforms that reinvent the iGaming industry.
About the team
We are a focused engineering team of 5 to 9 people at a multi-product tech SaaS company. We
work across personalization, recommendation, fraud detection, and some GenAI, through real-time, near-real-time, and batch pipelines. The team is a blend of Python backend engineers, ML engineers, QA (manual and automation), a data engineer, and data scientists. Our shared mandate is to take experimentation from our data scientists and turn it into robust, production-grade systems, either as standalone services or integrated into wider platforms.
The role
You will build and operate production backend systems alongside senior engineers and a hands-on engineering manager, owning your work end to end and growing into the harder problems.
The systems you work on are either part of a larger SaaS backend or run as standalone services: batch and event-driven pipelines, stream consumers, feature stores, REST APIs, scheduled jobs and background workers.
Responsibilities
- Design and build production Python services: APIs, stream consumers, pipelines, feature stores, scheduled jobs, background workers
- Write and tune the SQL and schemas these services depend on
- Uphold engineering standards: code quality, testing, observability, deployment discipline
- Productionize data scientists' experimentation into reliable services
- Diagnose and fix problems in production
- Contribute to design discussions and the implementation approach for the systems you work on
- Review code and help raise the bar for what the team ships
- Take part in the team's on-call rotation for the systems you build and operate
Requirements
- 3+ years of backend engineering experience
- Strong Python: async, data processing, and working with Redis, Kafka and database clients
- Deep database skills — non-trivial SQL, query plans, index design, transactions and isolation levels, and concurrency
- Experience across relational and non-relational stores, and when to choose which
- Good working knowledge of Docker and CI/CD, enough to partner well with DevOps
- Experience owning a service in production
Nice to have
- Graph databases (Neo4j or similar)
- PostgreSQL internals: MVCC, autovacuum and bloat, partitioning, connection pooling
- PySpark, Databricks
- Kubernetes
- Experience building LLM-powered systems, including the vector stores behind them
- Fraud detection, recsys, or personalization systems experience
- Prometheus and Grafana
- SaaS or high-scale product domain experience
Why join us
- Work on high-scale, high-throughput backend systems
- Broad range of technologies, stacks, and problems
- Take experimentation from data scientists and turn it into production systems
- A strong engineering culture that values technical depth and ownership
- A technically strong environment with a modern stack and a mature Agile culture
- High autonomy, decision-making authority, and close cooperation with leadership
- Room to contribute your own ideas for improvement and see them implemented
- Flexibility through hybrid and remote working
- A distributed team across Dubai and Europe
- Discretionary annual bonus, based on individual, team, and company performance
- Comprehensive medical insurance
- Annual flight allowance
- Visa sponsorship
- Annual leave
- Learning and conference budget
- Company-paid AI coding assistant
Interview stages
- 1. HR interview (30 minutes) — initial conversation about your experience, career goals, and cultural fit
- 2. Technical interview (1 hour) — in-depth technical interview covering relevant skills
- 3. Technical design interview (1.5 hours) — system and database design
- 4. Final interview (30 minutes) — a discussion with the hiring manager, focusing on role-specific competencies and alignment with company values
- 5. Reference check & job offer