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qlub is a fast-scaling fintech backed by Mastercard, Mubadala, e&, and Shorooq, with $72M raised. The company provides payment solutions for restaurants—Pay-at-Table, Digital Menus, Order-and-Pay, Payment Links, and SoftPOS terminals—enabling guests to pay via QR code in seconds without apps or downloads. Headcount has doubled in the past year, and the company operates across multiple markets globally.
In this role, you will lead product analytics at qlub and build the experimentation engine driving growth, scaling to 50+ A/B tests per month. You will own experimentation end-to-end: configuring and scaling the existing A/B testing platform, setting standards for test design, sample sizing, metrics and guardrails, and reviewing results. You will train product managers and operations leads to run sound experiments independently, building playbooks, templates, and review rituals to make experimentation routine.
You will define the analytical data model—entity and event design that joins consistently across product flows, slowly changing dimensions, a semantic layer, and metric definitions. You will introduce data contracts and governance standards with Tech and Data Engineering to ensure new events and tables are designed correctly from inception. You will coach and grow a team of three junior analysts and one senior analyst, fostering a culture of rigorous, data-driven decision-making.
You will drive change through influence and clear reasoning, partnering across Product, Operations, Tech, and Data Engineering. You will communicate clearly with executives and non-technical stakeholders, bringing an AI-first approach to analytics in a fast-moving, global fintech environment.
REQUIREMENTS:
- 5+ years in analytics or data science, including time at a large data-driven company (e.g., Delivery Hero, Uber, Amazon, Meta, Booking) or a modern data or experimentation platform (e.g., Sigma, Mixpanel, Amplitude, Statsig, Optimizely)
- Hands-on experience building or scaling an experimentation programme with solid statistics: power analysis, variance reduction, sequential testing, and common pitfalls
- Strong data modelling: dimensional modelling, slowly changing dimensions (SCDs), event schema design, dbt, and SQL at a high level
- Track record of driving change: introducing new practices into organisations and getting non-technical teams to adopt them
- Clear communication with executives and non-technical stakeholders
NICE TO HAVE:
- Data science and predictive modelling, including forecasting
- Experience building an AI-first data stack and taking an AI-first approach to analytical problems
- Data governance frameworks, marketing analytics, and attribution
- Experience with tools such as dbt, Airflow, Git, Google Cloud Storage (GCS), BigQuery, or ClickHouse
- Familiarity with data governance and cataloguing tools such as Atlan, Alation, or OpenMetadata
