About the job
About Airalo
Welcome to Airalo, the pioneering eSIM store that empowers travelers to connect seamlessly in over 200 countries and regions worldwide. As a travel-tech innovator, we are on a mission to transform the telecommunications landscape. At Airalo, we pride ourselves on fostering a diverse and inclusive workplace, where every team member contributes to our collective success. Our team spans across more than 50 countries and six continents, united by our commitment to revolutionizing connectivity.
Discover more about our culture and values in our Public Handbook: https://airalo-public.notion.site/airalo-public-handbook
About You
We are seeking a candidate who is passionate about delivering high-quality work and values the significance of collaboration and team success. You are a self-motivated individual who thrives without the need for micromanagement. Your authenticity, clarity in communication, and kindness are key attributes. You excel in managing multiple projects, possess strong analytical skills, and have a keen eye for detail. You embrace cultural diversity and are open-minded and tolerant of different perspectives.
About the Role
Position: Full-time / Employee
Location: Remote-first
Benefits: Health Insurance, work-from-anywhere stipend, annual wellness and learning credits, an all-expenses-paid company retreat in beautiful destinations, among other benefits.
We are in search of an Analytics Engineering Manager to spearhead our self-service analytics infrastructure and data modeling practices at Airalo. You will be responsible for establishing the foundational elements that facilitate scalable analytics, including the semantic layer, core data models, and dashboards. Your leadership will empower teams across our business to independently address their analytical needs. This is a pivotal role where you will define our data modeling strategies, govern metrics, and implement self-service analytics capabilities for a business serving over 20 million users across 190 countries.
You will report to the Director of Data and collaborate closely with analytics teams and stakeholders to convert their analytical requirements into robust, production-quality data models. Your success will be measured by the confidence of business users in addressing their inquiries, the establishment of a trusted semantic layer for analytics teams, and the deployment of self-service capabilities across the organization.
