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Gaming1 ищет инженера по данным для разработки надёжных и масштабируемых дата-продуктов, поддерживающих принятие решений на всей компании. Роль предполагает гибридную занятость с возможностью удалённой работы до 50% времени, и работу в международной, совместной среде игровой индустрии.
Идеальный кандидат обладает опытом 3–6 лет в data engineering, сильными навыками SQL и PySpark, а также знаниями Kimball-стиля модельирования данных. Знание Power BI и Airflow будет плюсом.
With 30 years of history, today Gaming1 is one of the international leaders in both land-based and online games of chance (casino games, sports betting and poker). Its evolution at the heart of innovation is accompanied by a diversification and specialization of positions, with the creation of new cutting-edge jobs. Gaming1 is looking for a Data Engineer to help us build reliable, scalable data products that support decision-making across the company while offering you a transparent career ladder and support to grow into your next role.
Build data products: design and develop pipelines and models in SQL and PySpark, from ingestion to analytics-ready tables. Model data: create clear dimensional models that analysts and Power BI reports can use directly. Own quality: implement tests based on business rules, investigate failed loads and improve reliability. Improve what exists: tune jobs, evolve schemas and pipelines, reduce compute costs, and leave the codebase better than you found it. Share knowledge: document your work in the project’s Git repository so it is easy to understand, maintain and reuse.
Strong SQL skills and the ability to write and optimise complex queries. Experience with PySpark (or Python) in a data-engineering context. Solid data-modelling knowledge, including Kimball-style star schemas. Around 3-6 years of hands-on data-engineering experience, with the autonomy to own work from source to reporting-ready data. A collaborative mindset, clear communication skills and comfort working in the gaming industry. Professional English.
Power BI experience, including semantic modelling, reporting or governance; Experience working in a data-mesh or other decentralised data organisation; Airflow or a similar orchestration tool; Automated data-quality testing, such as Great Expectations, Soda or dbt tests