Senior AI Data Expert (f/m/x)

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 Data Expert (f/m/x), you'll operate at the intersection of data engineering, machine learning, data science, and analytics, building and scaling the data foundation that powers our entire business. This role combines all four data disciplines into one generalist position, you move to wherever the highest-value data problem is, rather than staying inside one lane. 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.

• Analysis & Insight: You also turn open business questions into decision-ready analysis when that's where the highest-value work is, not just build the pipelines that feed it.

• 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.

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• 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 move fluidly across data engineering, machine learning, data science, and analytics work, depending on where the team needs you most, rather than staying fixed in one discipline.

• 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.

• Technical Foundation: You have a degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or bootcamp experience, with a minimum of 4 years of professional experience across data engineering, machine learning, data science, or analytics.

• Cross-Disciplinary Range: You have real experience in at least two of data engineering, machine learning, data science, or analytics, and you're comfortable picking up the others as the role demands.

• Current, Deep Databricks Experience: You are proficient in designing, implementing, and optimizing ETL processes on Databricks, with at least a year of current, hands-on depth, not brief or dated exposure.

• Tools & Technologies: You have hands-on experience with Databricks and AWS services such as S3, Kinesis, and Lambda or similar technologies. Experience with GitLab CI/CD pipelines and familiarity with PostgreSQL are a plus.

• Genuine AI Fluency: You use AI tools like Claude hands-on, daily, across planning, building, debugging, and documentation, not just for occasional testing. This is a core, non-negotiable part of the role, not a nice-to-have.

• Context Engineering: You know how to set up and maintain project context and memory for AI coding tools, structured instructions and reusable prompts, so they stay effective as a codebase grows rather than relying on default settings.

• Agentic & Worktree Fluency: You're comfortable running several tickets in parallel with AI assistance, using Git worktrees or a similar approach to keep parallel AI-assisted branches isolated and reviewable, and you can explain your actual workflow in detail rather than describing it as something that just happens automatically.

• Programming Skills: You are highly proficient in Python, SQL, and PySpark for data manipulation and processing, and you write clean, maintainable, production-ready code.

• Mindset & Communication: You have excellent problem-solving skills, strong attention to detail, and the ability to work independently and collaboratively. You are fluent in English, both verbally and in writing, comfortable letting AI take on a meaningful share of day-to-day coordination, and genuinely open to a way of working where AI reshapes how data teams operate, not just tolerant of it.