Principal Analytics Lead (m/f/d)

About this role

A career driven by you. Join AutoScout24 Group and experience life in the fast lane. We’re not just leading the market; we’re creating it. We’re constantly innovating, evolving, and transforming things for our customers. We’re driven, ambitious, and determined to do our best work together. For a career that doesn’t stand still, where you can put your ideas in the driving seat and change the way the world moves, join our international community.

Global Central Analytics is expanding its established marketplace reporting capabilities across the Autonex group. We are looking for a Principal Analytics Lead to build our Portfolio Intelligence capability for AutoProff, DealerTrack, CMS, Software and Finance. You will help business leaders understand performance through trusted core KPIs and reporting that reflects how each business operates. You will report to the Central Analytics Lead, shape technical direction and work directly on complex analytical challenges.

Just as importantly, you will help colleagues develop their skills and improve how the team delivers insight across the business. AI is embedded in our everyday work. You will use AI tools actively in analysis and development, help colleagues adopt effective workflows, and apply sound judgment to the results. What you’ll do Shape the analytics priorities and reporting approach for portfolio businesses, connecting their commercial drivers to the decisions leaders need to make.

Coach analysts through problem framing, pairing, reviews and constructive feedback. Help colleagues develop their technical judgment, stakeholder communication and confidence in owning complex work. Raise standards across Global Central Analytics by sharing best practices, creating reusable approaches and turning lessons from projects into guidance the wider team can apply. Identify opportunities to improve how the team works: simplify reporting, reduce manual effort and technical debt, and improve reliability, delivery speed and the cost of analytical workloads.

Make AI part of everyday delivery, from SQL and Python development to exploration, testing, documentation and reporting. Evaluate new tools and agentic workflows, share what works, and validate results against trusted data. Lead complex reporting initiatives from requirements to adoption, staying hands-on with data models, pipelines and dashboards and partnering with engineering and data platform teams. Establish clear KPI definitions, ownership and validation, with documentation and controlled changes that preserve comparability.