Senior Data Analyst

Company Description At Docplanner Group, we’re on a mission to help people live longer, healthier lives. As the world’s largest healthcare platform, each month, we connect 24 million patients with 280k doctors across 13 countries (through brands like ZnanyLekarz, Doctoralia, MioDottore, DoktorTakvimi, and jameda). Our marketplaces, SaaS and AI tools simplify daily tasks and help doctors, clinics and hospitals work more efficiently, so they can focus on what really matters: caring for their patients.
Learn more about our products here: pro.doctoralia.es
Why join us? Real impact – We help doctors help patients. Your work truly makes a difference.
At scale, yet agile – 3,000+ employees, but still fast, flexible, and hands-on.
Shape the future, sustain growth – Make a difference now and build for long-term success.
Job Description Docplanner's Customer Success organisation serves hundreds of thousands of healthcare professionals across 13 markets. Two global business lines sit at the centre of that effort, Individual Doctors and Clinics, each led by a VP who needs sharp, reliable analytics to grow their business and make decisions with confidence.
This role sits within Global Customer Success Operations & Analytics, the team that acts as the connective layer between the business and Docplanner's data organisation: BI (dashboard and data standardisation), Data Platform (infrastructure, architecture, DWH), and Product Analytics (product launches and opportunities).
This role is dedicated to one business line, partnering closely with that VP and their leadership team on the deep dives, data models, dashboards, and experiments needed to run and grow the business across our 13 markets.
You’ll report to the Global CS Ops analytics manager, who sets the team's priorities and supports big projects the team takes on. Within that frame, you own how your own time is spent day to day, prioritizing incoming asks, sequencing your backlog, and deciding what needs your manager's input versus what you can resolve directly with your business and data stakeholders.
Reporting, dashboards & data models Build and maintain the recurring reporting, dashboards (Tableau, superset), and data models your business line runs on
Build and maintain automations that scale across markets
Keep outputs consistent with BI's dashboard and data standards, escalating gaps rather than working around them
Analytical models: statistics & prediction (a growing focus) Today, most of your time goes to reporting and execution. Over the next year, we expect a meaningful shift toward analytical modelling (statistics and prediction)
Review, update, and improve the statistical and predictive models your business line already relies on
Build MVPs for new predictive or statistical use cases, handing them off to Data Science once validated
Bring regression, forecasting, and other statistical techniques into your day-to-day analysis, not just descriptive reporting
AI-first execution Work AI-first: use AI to query, code, and analyse faster and more consistently
Use and contribute to the team's shared libraries, reusable code, and architecture (e.g. GitHub-based repositories)
Bridging business and data teams Act as the translation layer between your business line and Docplanner's data organisation: BI, Data Platform, and Product Analytics
Bring the right requirements to each team, and flag when scope or timelines don't match business priority
Driving analytics for your business line Partner directly with the VP of your assigned business line and their team to understand the business, identify gaps, and surface opportunities
Lead deep dives that go beyond describing what happened to explain why, and what do next
Propose, run, and analyse experiments (A/B tests and beyond) that inform business decisions
Prioritisation & stakeholder alignment Own the day-to-day sequencing of your own workload: triage incoming requests, judge urgency and impact, and decide what to tackle first without waiting to be told
Align directly with assigned business and data stakeholders on what matters and when
Escalate cross-team or resourcing trade-offs to your manager, who owns prioritisation of the team's big projects
Qualifications What will help you thrive? Degree in a quantitative field (Engineering, Statistics, Mathematics, Physics, or similar)
+3-5 years of relevant analytics experience as an individual contributor
Very good SQL skills (Amazon Redshift preferred); solid Python, especially for statistical and predictive modelling
Working knowledge of statistical modelling (regression analysis, forecasting/prediction, A/B testing)
Hands-on experience with Superset and/or Tableau, building dashboards for non-technical audiences
Skilled at digesting complex data into business language: able to summarise findings and frame them as a clear narrative, not just a report
Experience w