Senior Data Analyst (Customer Success Operations)

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.

How will you make an impact?

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 architect