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5 часов назад

Lead AI Data & Analytics Engineer

Формат работы
remote (только Europe)
Тип работы
fulltime
Грейд
lead
Английский
b2
Страна
Europe/Germany
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Lead AI Data & Analytics Engineer (AI/game analytics): Building the end-to-end data foundation for a new mobile game, from event tracking and warehouse architecture to validated semantic models, reporting, and AI-powered analytics with an accent on agentic workflows, data correctness, and gameplay and monetization intelligence. Focus on designing greenfield pipelines, reviewing AI-generated code and queries, detecting anomalies, and translating player behavior data into production decisions.

Location: Hamburg, Germany / Europe; fully remote

Company

hirify.global develops and self-publishes data-driven, live-service strategy games in-house and is part of Stillfront Group.

What you will do

  • Design and operate the end-to-end gameplay analytics ecosystem for a new mobile game.
  • Choose and build the tracking, storage, processing, warehouse, reporting, and semantic-layer architecture from scratch.
  • Develop AI-driven reporting, dashboards, insight-generation workflows, and agentic analytics tools.
  • Review AI-generated code, queries, and analytics on critical paths while owning data correctness and quality decisions.
  • Identify trends, risks, opportunities, and anomalies in gameplay, monetization, attribution, and player behavior data.
  • Provide actionable recommendations to product, balancing, design, and leadership teams and establish standards for agentic engineering practices.

Requirements

  • At least one experience building an analytics or data platform from scratch, including choosing the stack, setting up the warehouse and pipelines, and taking it into production; extending an existing setup alone is not sufficient.
  • Several years of production experience building analytics or data-driven systems, ideally in gaming or mobile.
  • Experience with free-to-play mobile game analytics, ideally covering a launch from soft launch to global release and user-acquisition scaling.
  • Production experience with LLMs, agents, AI-assisted engineering, and agentic workflows; ability to demonstrate shipped agentic analytics used for real decisions.
  • Ability to explain prompt and context design, autonomous and multi-agent workflows, intervention points, MCP servers, custom tools, skills, subagents, and review of AI-generated code and queries.
  • Strong experience with behavioral, transactional, or event-based datasets and deriving actionable insights from complex data.
  • Strong Python and SQL skills, with the ability to independently read, verify, and own code and outputs.
  • Experience designing scalable data architectures, pipelines, data models, dashboards, analytical frameworks, or automated insight-generation systems, plus familiarity with cloud-based data platforms and storage.
  • Strong analytical decomposition, data-quality, reliability, independent-verification, ownership, and technical initiative skills; passion for gaming.
  • Excellent English communication skills; English is the working language.

Nice to have

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

Culture & Benefits

  • Fully remote work is available alongside the Hamburg studio location.
  • Work in an in-house environment with relevant game-development disciplines under one roof.
  • Build a greenfield data and AI foundation with agents as part of the engineering workflow.
  • Work on long-term intellectual-property growth and live-service game development within Stillfront Group.

Hiring process

  • Walk through an agentic workflow that has actually been built and used, including its specifications, context, loop structure, failure points, detection methods, and tasks that must not be delegated unsupervised.
  • Bring screen shares, repositories, prompts, or other evidence of the real implementation; judgement and practical experience are emphasized over polished slides.

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