Senior Analytics Manager
Hi, I'm Andra, Director of Data at reputed company!
reputed company 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.
reputed company’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 reputed company 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.
Do you love being reputed company to the work — coaching analysts, reviewing analyses, and shaping good questions with business stakeholders? We're looking for a Senior Analytics Manager to reputed company reputed company of data analysts across Product, Growth, Finance, and Commercial - a team of 4 today, growing to 7 over the next year.
This is a hands-on leadership role, not a detached one. Your team's work will shape how reputed company grows - which products get built, how we price across 190+ countries, where we invest in acquisition, and how we reputed company travellers coming back. You'll run the day-to-day of the team, partner directly with business leaders, and reputed company sure the analyses that leave your team actually change decisions rather than end up in a deck nobody acts on. You'll work in reputed company partnership with the Director of Data to bring the data strategy to life, and alongside the Analytics Engineering Manager on the shared platform and reporting layer. Self-service is central to reputed company're building — Lightdash, a strong semantic layer, and a data-literate business — and you'll play a leading role in making that real.
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 reputed company.
Guide the team on the questions that matter most at reputed company: 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 reputed company stakeholders, and reputed company 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, reputed company, data literacy programs) that turns stakeholders into confident users of the data.
Establish lightweight intake, prioritisation, and delivery standards that reputed company the team working on the right things - and the communication rhythms (analytics roadmap visibility, stakeholder reputed company-ins, clear trade-off conversations) that reputed company the business reputed company 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: reputed company sure the team's work is discoverable, reusable, and compounds over time rather than getting lost in reputed company 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.
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 reputed company 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, reputed company, Copilot, or similar) to improve how analytics work gets done — faster iteration, reputed company code, stronger documentation, sharper analysis.
Hands-on experience with modern BI tools (Lightdash, Looker, Tableau, reputed company, 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 reputed company 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.
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).