Top eSports Data Engineer Jobs
We are looking for a Data Engineering Manager to lead a team of Data Engineers and provide a best-in-class product experience for our customers. You will work closely with Product Managers, Scrum Masters, and Data Engineers to optimize and deliver innovative solutions.
As a Senior Data Engineer with a focus on data UX/visualization, responsible for designing and maintaining data pipelines, creating intuitive visualizations, and collaborating with cross-functional teams to drive actionable insights. Responsibilities include data pipeline development, data modeling, data visualization, user experience enhancement, performance optimization, data analysis, collaboration, quality assurance, technical leadership, staying updated on industry trends.
2K Games is seeking a Data Archivist Manager to manage archival processes for digital and physical game title data, maintain the Game IP Asset Index, and collaborate with internal and external teams. Responsibilities include developing archival processes, conducting research, preparing title content, performing managerial work, and quality control of metadata records. The ideal candidate should have strong organizational and communication skills, attention to detail, and the ability to work independently and in partnerships for big data projects.
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As an Associate Data Science Engineer at PrizePicks, you will be responsible for developing and maintaining streaming data and MLOps infrastructures to enable real-time priced markets within the product. You will collaborate with various teams to ensure data reliability, model output quality, and scalability.
Join Skillz as a Senior / Lead Cloud Database Administrator (Cloud DBA) and be part of a team revolutionizing the gaming industry. Enjoy comprehensive benefits, wellness support, and a culture of impact at Skillz. Work with industry pioneers and contribute to the future of gaming!
Design, develop, and maintain data pipelines and automated data processing for sports analytics platforms.
Lead the data practice at Caffeine by owning data backend engineering, data engineering, applied data science & machine learning, and analytics. Design and build data platform, lead data infrastructure, develop team members, and work on content discovery and personalization features leveraging ML and data-driven decisioning.
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