Senior Product Owner (Functional) – Data & Analytics

Role Overview
The Senior Functional Product Owner (Data & Analytics) owns the end-to-end delivery of data products, analytics solutions, and enterprise data capabilities.

The role bridges business, data engineering, and analytics teams to deliver trusted, high-quality data and actionable insights.
Key Responsibilities


* Define and own data product strategy, roadmap, and value realization
• Translate business needs into KPIs, metrics, and data models
• Design data flows from source systems to analytics consumption
• Own backlog across data pipelines, models, and BI dashboards
• Define data quality rules (accuracy, completeness, consistency)
• Collaborate with data engineers, BI developers, and architects
• Ensure delivery of high-quality dashboards and analytics
• Lead UAT including reconciliation and KPI validation
• Manage stakeholders and communicate data insights
• Track product value via adoption, quality, and business impact
• Enable advanced analytics use cases (AI/ML, predictive insights)
• Mentor team members and promote data literacy

Required Skills & Qualifications


* 8-15+ years in Product Ownership / Business Analysis / Data roles
• Strong understanding of data modeling and KPI frameworks
• Experience in Agile/Scrum environments
• Strong stakeholder management and communication skills

Technical Skills


* SQL knowledge (joins, aggregations)
• Data warehousing concepts
• BI tools (Power BI, Tableau, Looker)
• Familiarity with cloud platforms (Azure, AWS, GCP)

Success Metrics


* Data accuracy and reliability
• Adoption of analytics solutions
• Reduction in data defects
• Business impact and stakeholder trust

Key Responsibilities1.

Data Product Strategy & Vision


* Define and own the roadmap for data and analytics products (dashboards, data models, semantic layers, data services, AI agents )


* Align data initiatives with business strategy, KPIs, and enterprise data vision


* Identify high-impact use cases:
+ Operational reporting
+ Advanced analytics / AI use cases
+ Decision intelligence


* Prioritize based on data value, business ROI, and user impact

2.

Data-Centric Requirement Management


* Lead discovery of data requirements across business domains


* Translate business questions into:
+ KPIs and metrics
+ Data models and datasets
+ Analytical outputs


* Define:
+ Metric logic (formulas, aggregations)
+ Data definitions (business glossary)
+ Transformation rules


* Ensure alignment on "single source of truth" metrics across the organization

3.

Domain Expertise (Preferred)


* Experience in one or more domains:



* Procurement / Supply Chain



* Finance



* Digital platforms



* Data & Analytic...