Airalo

Analytics Engineering Manager

Job Description

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.


What you'll Do
  • 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

    • Must have
  • 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

    • Nice to have
  • Experience in marketplace, B2C, or subscription/usage-based businesses
  • Previous work in low-maturity or greenfield data environments
  • Familiarity with our stack: dbt, BigQuery, Lightdash, Fivetran
  • Experience with marketing analytics use cases: attribution, LTV, cohort analysis
  • Previous experience at a scale-up that went through hypergrowth

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