Senior Product Data Analyst

AirHelp is a global tech company specializing in air travel protection. Since 2013, it has helped millions of passengers claim compensation for disrupted flights and protected over 12 million travelers through its membership program AirHelp+. The company has a global team of over 400 employees speaking 19 languages and advocates for fair air passenger rights through APRA.

• Proven experience in data analytics within product engineering environments, collaborating closely with Product Managers.
• Proficiency in SQL for data cleaning, extraction, and manipulation.
• Fluency in behavioural analytics platforms and Google Analytics.
• Experience designing and delivering A/B tests using platforms like GrowthBook or Optimizely, with understanding of sample sizing, novelty effects, and segmentation.
• Experience working in AI-native development environments using agentic coding tools such as Claude Code and Codex.
• Ability to apply a product-oriented mindset to analytics and constructively challenge product strategy using data-driven perspectives.
• Bonus: Experience with dbt and data modelling, working closely with data teams on AI data product pipelines.

• Embed within product engineering squads to provide strategic analytical support, partnering with Product Managers and engineers to define hypotheses, measure feature performance, and inform product direction through data-driven insights.
• Conduct analyses to understand traveller needs and behaviours across their journey.
• Improve AirHelp mobile app monetisation by identifying friction points in claims submission and AirHelp Plus sales.
• Lead the end-to-end data curation process to capture, clean, and expose behavioural and transactional data powering AI products.
• Collaborate with data engineers to build AI-powered analytics workflows enabling product self-service.
• Partner with engineers on event tracking, instrumentation, and data ingestion to ensure measurement is integrated from feature launch.
• Develop deep funnel and cohort analysis across the customer lifecycle to inform optimisation and product strategy.
• Contribute to analytics craft through systems thinking, knowledge sharing, and raising data usage standards across product teams.