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Data Product Owner III

Posted about 1 month agoViewed

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💎 Seniority level: Middle, 3-5 years

📍 Location: Mexico, Brazil

🔍 Industry: Insurance

🏢 Company: Capgemini👥 10001-350000IT Services and IT Consulting

⏳ Experience: 3-5 years

🪄 Skills: AWSSQLAgileCloud ComputingGCPProduct ManagementSnowflakeData engineeringAgile methodologiesCross-functional collaborationData visualizationStakeholder managementData modeling

Requirements:
  • Proven experience as a Product Owner, ideally in data or analytics domains.
  • Strong understanding of data engineering, data architecture, and cloud platforms (AWS, Azure, GCP).
  • Familiarity with SQL, data modeling, and modern data stack tools (e.g., Snowflake, dbt, Airflow).
  • Excellent stakeholder management and communication skills across technical and non-technical teams.
  • Strong business acumen and ability to align data products with strategic goals.
  • Experience with Agile/Scrum methodologies and working in cross-functional teams.
  • Ability to translate data insights into compelling stories and recommendations.
  • 3-5 years experience in an Agile environment.
  • 3-5 years experience in Product Owner role or minimum 1 year as a PO II.
  • Demonstrated leadership experience.
Responsibilities:
  • Define and communicate the vision, roadmap, and backlog for data products.
  • Manages team backlog items and prioritizes based on business value.
  • Partners with the business owner to understand needs, manage scope and add/eliminate user stories while contributing heavy influence to build an effective strategy.
  • Translate business requirements into scalable data product features.
  • Collaborate with data engineers, analysts, and business stakeholders to prioritize and deliver impactful solutions.
  • Champion data governance, privacy, and compliance best practices.
  • Act as the voice of the customer to ensure usability and adoption of data products.
  • Lead Agile ceremonies (e.g., backlog grooming, sprint planning, demos) and maintain a clear product backlog.
  • Monitor data product performance and continuously identify areas for improvement.
  • Support the integration of AI/ML solutions and advanced analytics into product offerings.
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