Senior AI-Native Data Engineer (f/m/x)

exmox·Hamburgsenior

In short

→Ingeniero de datos senior que construye y escala plataformas de datos con IA integrada desde el principio.
→Trabajas con Databricks, PySpark y herramientas de IA como Claude para automatizar todo: desde código hasta documentación y colaboración.
→El enfoque es altamente agresivo: usar IA como co-ingeniero para acelerar el impacto, no como un añadido opcional.

Fluent English required for collaboration and documentation.

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What they ask for

✓4+ años de experiencia en ingeniería de datos
✓Experiencia profunda con Databricks y PySpark
✓Capacidad para usar IA (como Claude) como parte central del flujo de trabajo
✓Experiencia con versionado de código y worktrees en Git
✓Habilidades de colaboración con equipos de ingeniería y ML
✓Enfoque orientado al impacto, no al proceso

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

DatabricksPySparkGitClaudeSlackData LakehouseETLVersion ControlAI AgentsCLAUDE.md

Who should you write to at exmox?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

Your Mission This is a company built for growth. When you join exmox, you're stepping onto a global, highly competitive playing field, building high-performing consumer products in mobile gaming. We're scaling a rewarded user engagement and acquisition platform that helps publishers acquire and retain players, and here, data isn't just analyzed, it's turned into systems that directly drive business performance. As a Senior AI-Native Data Engineer (f/m/x) , you'll operate at the intersection of data, engineering, and product, building and scaling the data foundation that powers our entire business. This isn't a role for maintaining pipelines or following predefined processes, we look for engineering mindset and entrepreneurial ownership: you build it, you own it, and you see the impact in production. AI is not a side tool here, it's core to how you work: you'll use Claude and similar AI tools daily to plan, build, debug, and document, including managing context and memory across projects, integrating AI directly into your version control workflow, and running parallel agent workflows on separate tasks, cutting repetitive tasks so you can focus where it actually moves the needle. We believe a small team of engineers working closely with AI can outperform a much larger traditional data team, and we're building this role around that belief, not around AI as an occasional convenience. If you're excited by complex systems, high traffic, and data-driven decisions, and impact matters more to you than process, we want to hear from you. We use AI for everything in order to improve speed and quality. 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. 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. What You Bring Technical Foundation: You have a degree in Computer Science, Engineering, or Information Systems, or bootcamp experience, with a minimum of 4 years of professional experience in data engineering.

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