Senior Analytics Engineer, Pricing

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

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