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Product Owner
Newnan, GA
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View More Jobs Group Technical Product Owner United States (On-site) Be the First to Apply Apply Now Job Description About the Role We are seeking a Group Technical Product Owner to lead a portfolio of enterprise data products that power analytics, operational reporting, financial insights, customer intelligence, and AI/ML use cases across the organization. This role sits at the intersection of Product, Data Engineering, and the Business. You will own data product roadmaps, define governed data models, and ensure our modern data platform delivers measurable business outcomes — not just dashboards. You will operate at a portfolio level, driving reusable, scalable data products across domains such as: Financial & revenue reporting Sales & customer performance Asset & transactional data Behavioral / clickstream data Operational metrics This is a strategic role focused on building trusted, AI-ready, enterprise data foundations. What You'll Do Own Data Product Strategy & Roadmaps Define and manage roadmaps for a portfolio of enterprise data products. Translate business problems into scalable, reusable data models. Ruthlessly prioritize based on business value, architectural integrity, and long-term platform health. Drive alignment across Product, Engineering, Analytics, and Business leaders. Lead Technical Data Product Execution Partner with Data Engineering to design governed data models, pipelines, and semantic layers. Own backlog refinement and acceptance criteria for data product increments. Ensure consistent metric definitions, lineage, and data quality standards. Balance short-term reporting needs with long-term scalability. Establish Governance & Self-Serve Guardrails Drive adoption of a governed semantic layer as the source of truth for enterprise KPIs. Reduce metric duplication and fragmented reporting logic. Enable self-service analytics within structured guardrails. Define and enforce data contracts and standards across domains. Enable AI & Advanced Use Cases Ensure data products are structured to support personalization, predictive models, and AI/LLM initiatives. Partner with analytics and data science teams to provide high-quality, reusable inputs. Promote an AI-ready data ecosystem through strong modeling and governance.