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.
As Senior AI-Native Data Engineer (f/m/x) you won't just be maintaining pipelines or following predefined processes. You'll build and scale the data foundation that powers our entire business, with an engineering mindset and entrepreneurial ownership: you build it, you own it, and you see the impact in production. At exmox, we're embracing AI as part of how we work, not just what we build, and in this role it is core to everything you do: you'll use Claude and similar tools daily to plan, build, debug and document, and run parallel agent workflows on separate tasks. We believe a small team working closely with AI can outperform a much larger traditional data team. 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:
- Data Pipelines & Integration: You design, develop, and maintain scalable ETL pipelines on Databricks to integrate data from various sources, including app and web products and marketing partner data, ensuring reliable and efficient data flow across the business.
- Data Lakehouse Ownership: You build and manage the Data Lakehouse on Databricks, including migrating legacy data products, and implement data transformations and processing logic using Databricks and PySpark.
- Monitoring & Optimization: You monitor and maintain the data stack for nightly and near-real-time processing of in-house tracking solutions, continuously optimizing and troubleshooting pipelines to ensure data quality and integrity.
- AI-Native Workflow: You use Claude and similar AI tools daily, not occasionally, to plan, build, debug, and document your pipelines, treating AI as a core part of the job rather than an add-on.
- Version Control & Worktrees: You integrate AI directly into your version control workflow, using Git worktrees (or an equivalent) to run several AI-assisted branches of work side by side, rather than treating AI and your codebase as separate.
- Context & Memory Management: You maintain structured project context for your AI tools, for example a CLAUDE.md-style instructions file, so they stay accurate and useful as pipelines and codebases grow, rather than relying on ad hoc prompts each time.
- Parallel Agentic Work: You run multiple AI coding agents on separate tickets at the same time where it makes sense, reviewing and integrating their output rather than writing every line serially yourself.
- AI-Assisted Communication: You're comfortable using AI to help manage day-to-day coordination, including in Slack, when it's the faster and more reliable way to keep things moving.
- Cross-Functional Collaboration: You work closely with engineering and ML teams to ensure seamless data flow and integration into the Data Lakehouse.
- Staying Ahead: You stay updated with industry best practices and emerging technologies in data engineering and applied AI, bringing new ideas and approaches to the team.