Product Owner Data, Analytics & AI - Tieto Indtech (m/f/d)
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作为产品负责人,您是愿景与执行之间的纽带。您将产品战略转化为具体的开发项,并与跨模块团队一起确保交付。您与产品管理、其他产品负责人、开发人员和业务利益相关者紧密合作,将路线图分解为可执行的工作,维护团队待办事项,并代表客户和市场需求。凭借您对产品管理原则的理解和共同创新的思维方式,您使我们能够持续改进并交付有影响力、高质量的解决方案。除了……
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职位描述 · 原文
As a Product Owner, you are the link between vision and execution. You translate the product strategy into concrete development items and ensure delivery together with cross-module teams. Working closely with Product Management, other Product Owners, developers, and business stakeholders, you break down the roadmap into actionable work, maintain the team backlog, and represent customer and market needs. With your understanding of Product Management principles and a co-innovation mindset, you enable us to continuously improve and deliver impactful, high-quality solutions. Beyond internal alignment, you also engage directly with customers to gather insights, validate solutions, and drive co-innovation lead by the Product Manager. Your Mission Curate, prioritize, and maintain the team backlog, ensuring items are ready for planning and aligned with product strategy. Lead backlog refinement and collaborate with UX design, and engineering on specifications and testability. Define and uphold the Definition of Ready and Definition of Done with the team. Ensuring delivery of PI Objectives and identifying risks and dependencies. Accept or reject completed backlog items based on acceptance criteria and DoD. Monitor and report on team KPIs, ensuring transparency via backlogs, boards, and metrics. Ensure built-in quality, testability, and automation readiness across backlog items. Work with other Product Owners and Architects to manage interfaces, dependencies, and integration needs. Strive for continuous improvement in processes, practices, and product outcomes. What You Bring Proven experience as a Product Owner in an agile software development environment in the context of Data, Reporting and AI. Strong ability to translate product roadmaps into concrete backlog items and actionable requirements. Familiarity with agile frameworks (Scrum/SAFe) and backlog management practices. Solid understanding of Product Management and passion for co-innovation with customers. Excellent communication and stakeholder management skills—able to clarify intent, align priorities, and engage customers. Analytical, structured, and value-driven mindset with the ability to balance competing priorities. Fluency in English What We Offer Direct influence on the success of our product roadmap in a fast-growing, innovative environment. Continuous learning opportunities, mentorship, and personal ownership of key domain areas. A modern, flexible working setup and a culture that values transparency, co-creation, and experimentation. Close collaboration with Product Management, technology leadership, and customer stakeholders. Required domain knowledge Experience with live/near-real-time reporting , dashboarding, and analytical data models, including best practices for handling high-frequency operational data. Knowledge of modern data and AI platform architectures (e.g., event-driven data pipelines, message-based integrations, hybrid transactional/analytical patterns). Knowledge of data governance principles is a plus—data quality, lineage, access control, and metadata—especially in regulated contexts. Familiarity with Power BI , SQL Server (SSIS and SSAS) Experiences with data platform transformations (more modular with a clear product definition, Business Value driven) Ability to translate reporting, analytics, and AI use cases (e.g., performance insights, anomaly detection, process optimization) into concrete product requirements . Experience with Azure and AI tool stack (OpenAI / Azure AI Services) or similar tools, including model fine-tuning, prompt engineering, vectorization/embeddings, and responsible AI practices. Practical experience in delivering real data or AI products into production , not only prototypes or dashboards Understanding of MES data structures is a plus (event flows, and how operational data is generated, transformed, and consumed across modules) Understanding how operational data is generated, its limitations, and how variability in manufactu
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