Analytics Engineering Manager, Airalo

• We hope that you care deeply about the quality of your work, the intrinsic worth of tasks, and the success of your team
• You are self-disciplined and do not require micromanagement in terms of your skillset and work ethic
• You do your best to flourish as an individual every day while working hard to foster a collaborative team environment
• You believe in the importance of being — and staying — authentic, honest, positive, and kind
• You are a good interlocutor with clear and concise communication
• You are able to manage multiple projects, have an analytical mind, pay keen attention to detail, and love to get your hands dirty
• You are cognizant, tolerant, and welcoming of vulnerabilities and cultural differences
• 5+ years in analytics engineering, data engineering, or technical analytics roles, with 2+ years of people management experience-ideally building or scaling a team
• You're a hands-on leader who partners with senior leadership on strategy and priorities while owning execution and day-to-day team decisions
• Deep proficiency in dbt-you've built and scaled dbt projects, not just contributed to them
• Strong SQL and experience with at least one programming language (Python preferred)
• Experience implementing or heavily using a semantic layer / metrics layer (Lightdash, Looker, MetricFlow, or similar)
• Track record of driving self-service analytics adoption-training programs, documentation, stakeholder enablement
• Familiarity with dimensional modeling, data warehouse design patterns, and data quality frameworks
• Experience working closely with analysts and translating their needs into scalable data models
• Strong business acumen-you're driven to build scalable data products that deliver real impact, and you prioritise ruthlessly to get there
• Comfortable with ambiguity and greenfield data environments, with a passion for building team culture and raising the bar on data quality and usability
• Experience in marketplace, B2C, or subscription/usage-based businesses

Desirable

• We're looking for an Analytics Engineering Manager to lead our self-service analytics infrastructure and data modeling practice at Airalo
• You'll own the foundations that make analytics possible at scale: the semantic layer, core data models, dashboards, and the self-service platform (Lightdash) that enables teams across the business to answer their own questions
• This is a building role-you'll establish how we model data, how we govern metrics, and how we roll out self-service capabilities across a 20M+ user business operating in 190+ countries
• You'll report to the Director of Data and partner closely with analytics teams and stakeholders across the business, translating their analytical needs into scalable, production-quality data models
• Success looks like business users confidently answering their own questions, a governed semantic layer that analytics teams trust, and a self-service platform that replaces our patchwork of legacy reporting tools and robust data models that scale without use cases
• You'll lead and grow a team of analytics engineers (currently 2, scaling to 4 this year), building a culture of craft, documentation, and user empathy
• Drive the rollout and adoption of Lightdash as our single source of truth for business reporting, based on a unified KPI framework currently in progress
• Own all dashboard development initially - from executive reporting to operational views, with support from analysts - then fully transition the ownership to analysts as self-service matures, building the templates and processes that enable this shift
• Partner with stakeholders to translate reporting needs into well-designed, maintainable data products
• Design and deliver training and enablement programs for business users across all functions
• Own and evolve our core dbt models and semantic layer to support key analytical use cases: customer LTV, acquisition effectiveness, retention, funnel performance, and financial reporting
• Establish governance and standards: metric definitions, dashboard design patterns, modeling practices, testing frameworks, and documentation
• Partner with analysts to translate their needs into scalable data assets, and with Data Engineering on pipeline reliability and data quality
• Partner with Data Engineering on pipeline reliability, data quality, and infrastructure decisions
• Balance rigour with delivery speed-we're still building foundations while the business moves fast