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qlub is a fast-scaling fintech backed by Mastercard, Mubadala, e&, and Shorooq that removes friction from restaurant payments. Guests scan a QR code to pay in seconds with no app download required. The platform handles pay-at-table, digital menus, order-and-pay, payment links, and SoftPOS terminals. The company has raised $72M, operates across multiple markets globally, and doubled headcount in the past year.
You will lead product analytics and build the experimentation engine behind qlub's growth, scaling to 50+ A/B tests per month. You'll establish data modeling standards that analytics and AI can trust, and lead a team of BI analysts (three junior and one senior) working closely with Product, Operations, Tech, and Data Engineering.
Key responsibilities include:
- Own experimentation end-to-end: configure and scale the A/B testing platform, set standards for test design, sample sizing, metrics and guardrails, and review results
- Train product managers and operations leads to run sound experiments independently, building playbooks, templates, and review rituals
- Define the analytical data model: entity and event design, slowly changing dimensions, semantic layer, and metric definitions
- Introduce data contracts and governance standards with Tech and Data Engineering to ensure new events and tables are designed correctly from inception
- Coach and grow your team of analysts through direct, kind mentorship
You'll communicate clearly with executives and non-technical stakeholders, drive change through influence and reasoning, and bring an AI-first approach to analytics in a fast-moving global fintech.
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/experimentation platform (e.g., Sigma, Mixpanel, Amplitude, Statsig, Optimizely)
- Hands-on experience building or scaling an experimentation program with solid statistics knowledge: power analysis, variance reduction, sequential testing, and common pitfalls
- Strong data modeling skills: dimensional modeling, slowly changing dimensions (SCDs), event schema design, dbt, and SQL at a high level
- Track record of driving organizational change and getting non-technical teams to adopt new practices
- Clear communication with executives and non-technical stakeholders
NICE TO HAVE:
- Data science and predictive modeling, 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 dbt, Airflow, Git, Google Cloud Storage (GCS), BigQuery, or ClickHouse
- Familiarity with data governance and cataloging tools such as Atlan, Alation, or OpenMetadata
