Senior Data Analyst (Customer Success Operations)

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.

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?

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

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)

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

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

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 working cross-functionally, partnering with non-technical stakeholders across departments

Professional fluency in English (Spanish is a plus)

What makes you a strong fit?

Proactive by default: you spot what needs doing and raise the level of execution around you, rather than waiting for instructions

Owner with entrepreneurial mindset: own your work, your results, and everything in between

Able to prioritise your own workload with sound criteria, and know when to align with stakeholders versus escalate to your manager

Comfortable navigating ambiguity, driven by results

Understands the capabilities and limits of AI/LLMs and has experience building things with them (professionally or on your own)

Track record of scoping problems, running high-quality quantitative analysis, and developing insights that drive decisions

Communicates complex concepts simply and clearly; builds trust across a wide range of stakeholders

Experience building or validating statistical/predictive models and handing them off to