This is a company built for growth, not comfort.
When you join exmox, you're stepping onto a global, highly competitive playing field, solving real problems in environments that push you beyond your comfort zone. We move fast, cut the noise, and focus on what actually matters, because in our industry, speed wins.
With 50M+ platform downloads, we're rapidly scaling one of the most exciting innovations in mobile gaming: a rewarded user engagement and acquisition platform that helps players discover, and engage with games they enjoy - creating lasting value for both players and gaming studios.
At exmox, data is the compass that guides product, commercial, and financial decisions. We are moving away from siloed analyst roles and building interdisciplinary data teams where AI, specifically Claude, handles the heavy lifting of routine analysis, freeing the team to focus on judgment, framing, and impact.
As Data Manager (m/f/x), you will lead this transformation, building a team that operates across analytics, data engineering, data science, and ML rather than in silos, and using Claude and Databricks daily yourself rather than delegating that to others. You report to the Head of Data and lead a team of 6 Data Experts working on topics such as offer ranking, pricing, and ROAS prediction.
At exmox, we're also embracing AI as part of how we work, not just what we build. We use AI tools to work smarter, cut down on repetitive tasks and spend our time where it actually moves the needle. If that mindset speaks to you, you'll fit right in.
If you're excited by complex systems, high traffic, data-driven decisions, and the chance to grow by solving challenging problems, we want to hear from you.
What You'll Own:
- Build and lead a team structured around smaller, interdisciplinary pods that combine analytics, data engineering, data science, and ML, similar to how our engineering organization already works.
- Set the team's rituals and daily routine, and own how the team communicates, prioritizes, and collaborates.
- Own the delivery of your team's projects from scoping to measurable business impact, together with the team's Product Owner and the Feature Leads.
- Guide the team's shift from single-discipline work (analyst, data engineer, ML engineer or data scientist) work to a generalist model where Claude and Databricks handle routine analysis, freeing the team for higher judgment work.
- Stay genuinely hands-on with Claude and Databricks, including working with high frequency data that updates every few minutes rather than static daily aggregates.
- Manage across all four data disciplines at least from an understanding level, even without covering every technical topic personally.
- Partner with leadership to define target profiles, hiring needs, and pace as the team evolves.
- Develop your people: run regular 1:1s, give direct feedback, own performance and growth, and take an active role in hiring as the data organization scales.
- Champion real, hands on AI adoption across the team rather than surface level use.
What Success Looks Like:
- The team operates as true generalists: Analysts, data engineers, data scientists, and ML engineers work across disciplines rather than in silos, with Claude and Databricks doing the heavy lifting of routine analysis.
- Decisions are driven by evidence, not opinion: Leadership relies on your team's work before making major calls, and insights are translated into clear, decision-ready recommendations.
- The team's AI adoption is real, not surface level: Claude and Databricks are used daily as core tools, not occasionally, and this shows in the speed and depth of the team's output.
- People management and technical credibility go hand in hand: You are seen as a genuine technical leader as well as a strong manager, someone the team trusts on both fronts.
- The team delivers: Projects ship on schedule and show measurable impact on core KPIs.