Senior Analytics Engineer

We’re looking for a Senior Analytics Engineer to help shape how data is modeled and governed at Airalo, transforming first- and third-party data into clean, trusted, well-documented datasets that power decisions across product, marketing, finance, and commercial teams, serving 30+ million users and growing. This role carries particular weight in our finance and revenue domain: you’ll steward the models finance relies on directly, including order-to-revenue flows and reconciliation against third-party systems such as our ERP, where precision and auditability matter as much as scalability.

• Design, implement, and maintain data models using dbt within our cloud data warehouse, ensuring performance, cost efficiency, and alignment with business logic

• Build and maintain finance and revenue data models, including order-to-revenue flows, recognition logic, and reconciliation against source-of-truth financial systems, that finance and commercial teams can rely on for reporting and forecasting

• Own the order-to-revenue pipeline: ensuring every order correctly and traceably resolves into recognized revenue, with clear handling of refunds, adjustments, currency conversion, and edge cases

• Collaborate with other analytics engineers and data engineers to build and scale ELT workflows that ensure data freshness and reliability for mission-critical reporting

• Partner with Finance and the ERP systems integrator to validate that data models and definitions carry over correctly from legacy systems into the new ERP/EPM platform

• Implement robust testing, documentation, and monitoring practices, acting as a steward of data quality and governance across the platform, with particularly high attention to detail in domains where small errors compound (e.g., revenue, finance)

• Collaborate cross-functionally to translate complex business questions into scalable, production-ready data artifacts

• Partner with the data analysts and domain leadership to drive the rollout and adoption of self-service analytics, ensuring business users can trust and independently explore the data models you build

• Contribute to the evolution of our modern data stack, bringing curiosity and pragmatic judgment to new tools and approaches

• A Bachelor's/Master's degree in a quantitative field (statistics, economics, mathematics, computer science, or equivalent work experience)

• 5+ years in analytics engineering, data engineering, or analytics roles with significant modeling responsibilities

• Hands-on experience building finance and/or revenue data models, ideally including order-to-revenue or billing-to-revenue pipelines, revenue recognition logic, or financial reconciliation (ERP, billing platforms or similar)

• Strong SQL and data modeling skills, with demonstrated experience designing robust, scalable dimensional models

• Exceptional attention to detail, especially when working with numbers that feed financial reporting, reconciliation, or external disclosure

• 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

• Track record of partnering with analysts and business stakeholders to translate requirements into well-structured data products

• 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

• Experience in marketplace, telecom, or travel/mobility businesses

• Background in B2C product companies with high-volume event data

• Experience building analytics engineering capabilities in low-maturity data environments

• Previous experience at a scale-up that went through hypergrowth

• Experience defining and governing company-wide KPIs and metric frameworks

• Experience working alongside or within a finance function (e.g., supporting FP&A, revenue accounting, or audit processes) on data accuracy and traceability