Hi, I'm Andra, Director of Data at Airalo!
Our team works across the full data ecosystem, from collection to insights activation, ensuring that every piece of data drives meaningful action. We’re curious problem-solvers who love tackling challenges that haven’t been solved before and building tools and processes that scale impact across the company.
Airalo’s fully remote Data team in the UK is growing. You’ll transform data into decisions that shape the business, uncovering insights, solving complex problems, and influencing how we connect millions of travellers. With access to modern tools and the freedom to experiment, you’ll help turn analytics into action that drives real impact across the company.
We're looking for a Senior Analytics Engineer to join our growing Data team and help shape how data is modeled and governed at Airalo. You'll transform our first-party and third-party data into clean, trusted, well-documented datasets that power decisions across product, marketing, finance, and commercial teams - serving 20+ million users and growing.
Our analytics engineering function is still maturing, which means a real opportunity to establish standards, define our semantic layer, and influence how data is structured as we scale to serve 20+ million users globally. You'll work closely with the other data disciplines to build a production-grade analytics platform using Lightdash as our BI layer and beyond.
What You'll Do:
Build and own package-level unit economics data models that surface margin contribution, breakage rates, cost dynamics, and profitability across markets, corridors, and packages
Develop the analytical scaffolding for structured pricing reviews: margin waterfalls, competitive positioning dashboards, demand signal reporting, and conversion sensitivity analysis
Integrate competitive pricing data (scraped and third-party) into structured, queryable models that power automated benchmarking and price distance analysis across priority markets
Build and codify the data inputs that power our pricing decision framework—elasticity indicators, corridor-level competitive intensity, segment performance—enabling structured trade-offs between growth, margin protection, and competitive response
Own the semantic layer and metrics definitions for pricing and commercial domains, ensuring consistency and trust across Lightdash, downstream tools, and self-service analytics
Write Python-based tooling that goes beyond transformation: simulation models, scenario analyses, pricing rule engines, and automated competitive monitoring scripts
Design measurement-ready datasets that support A/B testing, controlled pricing pilots, and experimentation infrastructure as we build our capabilities for predictive pricing
Collaborate with data engineers to scale ELT workflows and improve CDR digestion pipelines, ensuring freshness, reliability, and full coverage for pricing-critical reporting
Partner with the Commercial Pricing Lead, Networks, Finance, and Growth to translate pricing strategy into data products, and ensure cross-functional stakeholders can independently explore and act on the models you build
Implement robust testing, documentation, and monitoring—you’re the steward of data quality in a domain where bad data means bad pricing decisions at scale
Must Have:
Bachelor's/Master's degree in a quantitative field (statistics, economics, mathematics, computer science, or similar)
5+ years in analytics engineering, data engineering, or analytics roles with significant modeling responsibilities
Minimum 2–3 years of direct experience working with pricing—whether in pricing analytics, commercial pricing, revenue optimization, or building pricing data infrastructure in telecom, SaaS, fintech, or marketplace environments
Proficient in Python for data transformation, analytical tooling, and automation (not just notebooks—you can build production-quality scripts and pipelines)
Strong SQL and data modeling skills, with demonstrated experience designing robust, scalable dimensional models—ideally in domains involving transactional data, cost structures, or commercial metrics
Significant hands-on experience with dbt (Core or Cloud) for managing transformation logic, testing, and documentation
Familiarity with cloud data platforms (BigQuery) and data orchestration tools (Airflow, Dagster, or similar)
Experience working with modern BI platforms and semantic layers—Lightdash experience is a strong plus
Experience with self-service analytics and empowering non-technical stakeholders to explore data independently
Strong communication skills, with the ability to explain complex data concepts to diverse audiences
Proactive self-starter who thrives on solving ambiguous problems and takes ownership from concept to production
