Lead AI Data & Analytics Engineer

Goodgame Studios

About The Position

Build the data foundation for a new mobile game approaching launch. This greenfield role owns the end-to-end gameplay analytics ecosystem, from selecting and setting up the data stack to defining how gameplay, monetization, attribution, and player behavior data are collected, stored, processed, and transformed into reliable insights and reporting. The role establishes technical direction and develops analytics and AI capabilities from concept through production. The position is AI-native: agentic workflows and AI-assisted engineering tools are used to increase throughput, while the engineer remains responsible for architecture, review, data correctness, and final decisions. Responsibilities include creating a semantic layer that translates raw gameplay data into validated business and gameplay concepts; developing AI-driven reporting, dashboards, and insight-generation workflows; and setting standards for tooling, review processes, quality gates, and completion criteria for agent-generated implementation. The engineer will identify trends, risks, opportunities, and anomalies in gameplay and player behavior, and provide clear, actionable recommendations to product, balancing, design, and leadership teams. Goodgame Studios is based in Hamburg, Germany, welcomes international applicants, and uses English as its working language.

Requirements

Has built an analytics or data platform from scratch at least once, including choosing the stack, setting up the warehouse and pipelines, and taking the platform into production; experience only operating or extending an existing setup is insufficient.
Several years of experience building analytics or data-driven systems in production environments; gaming or mobile experience is preferred.
Experience with analytics for free-to-play mobile games, ideally including a launch from soft launch to global launch.
Deep comfort with LLMs, agents, and AI-assisted engineering workflows in production environments.
Hands-on experience using agentic workflows and AI coding tools in real-world projects, with shipped agentic analytics work used for real decisions.
Ability to discuss how they structure prompts and context; manage autonomous and multi-agent workflows, including when to intervene; use MCP servers, custom tools, skills, or subagents; and review AI-generated code and queries without becoming a bottleneck.
Strong experience with behavioral, transactional, or event-based datasets and deriving actionable insights from complex data.
Strong Python and SQL skills.
Ability to read and own code they did not write and independently verify outputs rather than blindly trusting generated results.
Experience designing scalable data architectures, pipelines, data models, dashboards, analytical frameworks, or automated insight-generation systems.
Familiarity with cloud-based data platforms and storage.
Strong analytical decomposition skills and ability to translate ambiguous business questions into precise analytical problems.
Strong instincts for data quality, system reliability, and independent verification.
Comfort operating independently, driving technical initiatives from concept to production, and owning outcomes.
Drive to build something new at high speed and move it forward proactively.
Excellent English communication skills.
Passion for gaming.

Nice To Haves

Depth in monetization, economy, and LiveOps analytics.
Experience with event-driven and streaming data architectures.
Experience with real-time telemetry or anti-cheat/anomaly-detection systems.
Published or shared work on agentic workflows, such as blog posts, open-source subagents or skills, or internal tooling that has been open-sourced.

Responsibilities

Design, build, and operate the end-to-end gameplay analytics ecosystem.
Select and establish the data stack, including tracking, storage, processing, and reporting architecture.
Own data correctness and make judgment calls when data quality checks flag anomalies.
Build a semantic layer translating raw gameplay data into validated gameplay and business concepts.
Develop AI-driven automated reporting, dashboards, and insight-generation workflows.
Evaluate and implement AI tools, agents, and workflows to improve analytics speed, reliability, and depth.
Review AI-generated code and analytics on critical paths before production.
Identify opportunities, risks, anomalies, and trends in gameplay and player behavior.
Provide data-driven recommendations to product, balancing, design, and leadership teams, translating complex datasets into actionable insights.
Help define how the wider engineering organization adopts agentic practices and establish standards for tooling, review processes, quality gates, and completion criteria.