About TimeLeft

TimeLeft is reimagining how people meet. We create immersive social experiences

that help people break out of their routines and connect in the real world. Our

platform has grown rapidly across Europe, and we’re now expanding globally. It’s an

exciting time to join as we scale, experiment, and build the data foundation that will

power our next stage of growth.

The Role

We’re looking for our first Head of Analytics. A hands-on leader who can build the

analytics function from the ground up while setting the long-term data strategy.

In this role, you’ll start by diving deep into our stack: setting up experiments, fixing

tracking issues, and delivering trusted dashboards. Over time, you’ll define our

analytics vision, create scalable systems, and shape a culture where product,

marketing, and operations decisions are driven by data.

You Will

• Build the analytics foundations: design and maintain tracking, data pipelines, and unified data models
• Set up and scale experimentation: create an A/B testing framework and coach teams to test and learn faster
• Ensure data quality: put strong processes in place with engineering and QA to keep data clean and reliable
• Deliver dashboards and self-serve analytics: build clear, trusted dashboards for product, marketing, ops, and leadership
• Measure business impact: partner with product to evaluate the effect of new features on revenue and retention
• Provide insights and storytelling: deliver clear, actionable insights that influence strategic decisions
• Build and grow a team: hire, mentor, and scale a high-performing analytics team as TimeLeft grows

You Need

• 7+ years in data analytics, ideally in consumer tech, marketplaces, or

subscription models

• Proven experience building analytics infrastructure end-to-end (tracking,

pipelines, modeling, dashboards)

• Strong SQL, dbt, and BI tool skills (we use Lightdash)
• Experience with product analytics (we use PostHog) and attribution (we use

AppsFlyer)

• Deep knowledge of experimentation design, statistical analysis, and

subscription/marketplace metrics (ARPU, LTV, churn, cohorts)