Senior AI Data & Analytics Engineer

Goodgame Studios

About The Position

Support us in Agentic Analytics for a New Mobile Game! We're not looking for someone who has "built a dashboard". We're looking for someone who already uses agentic workflows as a core part of how they work: Claude Code, Codex, Cursor, custom agents, MCP servers, skills, planning loops, review gates, and AI-assisted engineering. Someone with strong opinions on which AI workflows hold up in production and which fall apart when real decisions depend on the numbers. You'll bring that practice to the AI-native analytics ecosystem of a brand-new mobile game. You'll define how gameplay, monetization, attribution, and player behavior data are collected, structured, and transformed into trustworthy insights, automated reporting, and AI-powered analytics tools. You will work at the intersection of AI, analytics, and game development, building the data foundation of a new game from scratch. You'll define its technical direction and drive the evolution of its analytics and AI capabilities from concept to production. Unlike a traditional analytics role, you'll use agents to dramatically increase your throughput while remaining the architect, reviewer, and final owner of correctness. The agents generate volume; you provide judgement. YOUR MISSION Build the Data Foundation Design and operate the end-to-end gameplay analytics ecosystem. Define tracking, storage, processing, and reporting architecture. Own data correctness and make judgement calls when quality checks flag anomalies. Drive AI-Native Analytics Build the semantic layer that translates 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 that improve analytics speed, reliability, and depth. Review AI-generated code and analytics on critical paths before they reach production. Generate Product Intelligence Identify opportunities, risks, anomalies, and trends across gameplay and player behavior. Support product, balancing, design, and leadership teams with data-driven recommendations. Translate complex datasets into clear, actionable insights. Set the Agentic Bar Help define how the wider engineering organization adopts agentic practices. Establish standards for tooling, review processes, quality gates, and what "done" means when agents generate most of the implementation.

Requirements

Several years of experience building analytics or data-driven systems in production environments, ideally in gaming or mobile.
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.
The ability to discuss concretely how you: structure prompts and context, manage autonomous and multi-agent workflows, including when to intervene, use MCP servers, custom tools, skills, or subagents, review AI-generated code and queries without becoming a bottleneck.
Strong experience working with behavioral, transactional, or event-based datasets and deriving actionable insights from complex data.
Strong Python and SQL skills.
You read and own code you did not write and verify outputs independently 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 the ability to translate ambiguous business questions into precise analytical problems.
Strong instinct for data quality, system reliability, and independent verification. You don't simply accept whatever the agent produces.
Comfortable operating independently, driving technical initiatives from concept to production, and owning outcomes rather than tasks.
Excellent English communication skills.
Passion for gaming.

Nice To Haves

Gaming or mobile analytics experience (monetization, attribution, retention, LiveOps, economy).
Event-driven and streaming data architectures.
Real-time telemetry or anti-cheat / anomaly-detection systems.
Published or shared work on agentic workflows – blog posts, OSS subagents/skills, internal tooling you've open-sourced.

Responsibilities

Design and operate the end-to-end gameplay analytics ecosystem.
Define tracking, storage, processing, and reporting architecture.
Own data correctness and make judgement calls when quality checks flag anomalies.
Build the semantic layer that translates 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 that improve analytics speed, reliability, and depth.
Review AI-generated code and analytics on critical paths before they reach production.
Identify opportunities, risks, anomalies, and trends across gameplay and player behavior.
Support product, balancing, design, and leadership teams with data-driven recommendations.
Translate complex datasets into clear, actionable insights.
Help define how the wider engineering organization adopts agentic practices.
Establish standards for tooling, review processes, quality gates, and what "done" means when agents generate most of the implementation.