Senior Analytics Engineer, Finance

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.

Our analytics engineering function is still maturing, so you’ll have real ownership in establishing standards, defining our semantic layer, and shaping how data is structured as we scale globally, working closely with the rest of the data team to build a production-grade analytics platform on LightDash and beyond.

What You'll Do:

• 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

Must Haves:

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

• 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)

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

• 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

Nice to Have:

• 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