Senior Analytics Manager Data Data Analytics Spain Spain United Kingdom Full-Time Ready to make travel easier for millions? Airalo is the world’s first and largest eSIM store, helping travellers stay connected seamlessly in over 200 countries and regions. We trust our teams to take ownership, put customers first, and do work that has a real impact every day. What’s in it for you? Airalo offers team members a range of perks, including remote work, generous PTO, wellness and learning allowances, and, of course, our annual Airalo Away retreat. Learn more about our benefits here; www.notion.so/airalo-public/Benefits-25396a97ffca81fb9bc1f0be479f1be3 Hi, I'm Andra, Director of Data at Airalo! Our team works across the full data ecosystem, from collection to insights activation, ensuring that every piece of data drives meaningful action. We’re curious problem-solvers who love tackling challenges that haven’t been solved before and building tools and processes that scale impact across the company. Airalo’s fully remote Data team is growing. You’ll turn numbers into decisions that shape the future of our business, collaborating with cross-functional teams to solve complex problems and influence how millions of travellers stay connected. This isn’t just dashboards - it’s using data to drive strategy, inform product and growth decisions, and create real impact. You’ll have access to best-in-class tools, the freedom to experiment, and a team ready to turn insights into action. By applying, you acknowledge and agree that, in case of successful application, Airalo may request to run background checks as a condition for entering into an agreement with you. Rest assured that these checks will only occur upon your prior consent and at the end of the selection process, and will be strictly limited to what is allowed under the laws that are applicable to you. All data that you share or that we collect in connection with such checks will be processed in accordance with our Privacy Policy, available here: www.airalo.com/more-info/privacy-policy?srsltid=AfmBOooBT0rXAj1FaNelZ3VfN0wvhwzvAoxdtHnOKSVETpiSjiXVuycy We sincerely thank all applicants in advance for submitting their interest in this opportunity. Airalo is an equal-opportunity employer and values diversity, equity & inclusion. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to providing reasonable accommodations upon request for individuals with disabilities throughout our job interview process. 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).

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