Responsibilities include, but are not limited to:

Manage and coach a growing team of 4 data analysts across Product, Growth, Finance, and Commercial - running the operating rhythm: planning, 1:1s, reviews, and career development.

Act as the go-to analytics partner for Product, Growth, Finance, and Commercial leaders - translating business questions into analysis plans, and findings into recommendations leaders can act on.

Guide the team on the questions that matter most at Airalo: funnel performance, retention, LTV, conversion, pricing, channel effectiveness, and the unit economics that drive the business.

Review analyses for rigour, clarity, and actionability before they reach stakeholders, and raise the bar on analytical craft across the team.

Drive adoption of Lightdash across the business in partnership with Analytics Engineering - building the enablement layer (training, documentation, office hours, data literacy programs) that turns stakeholders into confident users of the data.

Establish lightweight intake, prioritisation, and delivery standards that keep the team working on the right things - and the communication rhythms (analytics roadmap visibility, stakeholder check-ins, clear trade-off conversations) that keep the business aligned on what analytics is delivering and why.

Set standards for how analysis is scoped, delivered, and documented - balancing depth on strategic projects with responsiveness to the questions the business needs answered this week.

Build institutional knowledge: make sure the team's work is discoverable, reusable, and compounds over time rather than getting lost in ad-hoc threads.

Experience and understanding around product teams work with data - embedding analytics into product discovery, informing launch decisions, and running experimentation as a discipline that changes what gets built rather than just scoring it.

Must-haves:

7+ years in data analytics, product analytics, or data science, with at least 2 years of direct people management experience

Strong SQL and Python skills - you're fluent enough to pair with analysts on hard problems, review their code, and still write your own when it matters. Solid grasp of the modern data stack (BigQuery, dbt, semantic layers) and used to working closely with analytics engineering counterparts.

A track record of using AI-assisted tools (Claude, Cursor, Copilot, or similar) to improve how analytics work gets done - faster iteration, better code, stronger documentation, sharper analysis.

Hands-on experience with modern BI tools (Lightdash, Looker, Tableau, Metabase, or similar), ideally including rollout and adoption work.

Depth in at least two of - with a strong interest in learning and growing into the others - product analytics (funnels, feature adoption, experimentation), marketing and growth analytics (acquisition, channel performance, attribution, cohorts, LTV, CAC, ROAS), or finance analytics (forecasting, unit economics, planning).

Experience in a B2C tech environment - consumer apps, marketplaces, subscription products, or similar. You understand how consumer products grow, how users behave at scale, and the analytical rhythms of a business where millions of individual decisions shape the outcome.

Experience partnering with stakeholders at all levels - you can hold your own in a room with senior leaders, shape the questions before chasing the answers, and land recommendations that actually change decisions.

Comfortable operating in a low-maturity data environment - you've worked somewhere that didn't have clean metrics definitions, established workflows, or a strong stakeholder culture around data, and you helped change that.

A track record of shaping how product teams work with data - embedding analytics into product discovery, informing launch decisions, and running experimentation as a discipline that changes what gets built rather than just scoring it.

Bonus:

Experience leading self-service BI adoption or building data literacy programs.

Experience growing an analytics team through a hiring phase.

Familiarity with experimentation frameworks and A/B testing at scale.

Exposure to modern analytics engineering practices (dbt, metrics layers, KPI governance).