Product Owner

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 Responsibilities

1. 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 & Analytics

3. Functional Design of Data Solutions

• Design end-to-end data flows and functional architecture:
• Source systems → Data pipelines → Data models → Visualization
• Define requirements for:
• Data ingestion and processing
• Data transformations and enrichment
• Analytical datasets and semantic layers
• Collaborate with architects to align with data platform standards

4. Backlog Ownership for Data Products

• Own a multi-layered backlog, including:
• Data ingestion pipelines
• Data modeling (facts/dimensions)
• BI dashboards
• Data quality improvements
• Prioritize work considering:
• Business value
• Dependency on upstream systems
• Data availability and quality
• Ensure all stories include:
• Data logic
• Acceptance criteria (data validation rules, thresholds)

5. Collaboration with Data & Engineering Teams

• Work closely with:
• Data Engineers (ETL/ELT pipelines)
• Data Architects (data models, platforms)
• BI Developers (visualization layers)
• Data Scientists (ML/AI use cases)
• Translate business needs into clear, technically feasible requirements
• Ensure alignment between:
• Functional requirements
• Technical implementation
• Performance and scalability expectations

6. Analytics & Insight Delivery

• Ensure delivery of high-value analytics solutions, such as:
• Executive dashboards
• Operational reporting
• Self-service analytics
• Validate that insights are:
• Accurate
• Actionable
• Business-friendly
• Drive adoption of data-driven decision-making

7. UAT, Validation & Data Testing

• Define comprehensive data validation strategies, including:
• Source-to-target reconciliation
• KPI validation
• Historical comparisons
• Lead UAT with business teams to ensure:
• Trust in data outputs
• Business usability of analytics
• Ensure all data products meet acceptance thresholds

8. Stakeholder Management (Data-Focused)

• Engage with:
• Business leaders
• Functional SMEs
• Data governance bodies
• Translate technical data concepts into business insights and narratives
• Manage expectations on:
• Data availability
• Data limitations
• Delivery timelines

9. Value Realization & Performance Tracking

• Define and monitor success metrics for data products, such as:
• Adoption rate of dashboards
• Data quality improvements
• Business impact (revenue, cost savings, efficiency)
• Establish feedback loops for continuous improvement

10. Advanced Analytics Enablement

• Identify opportunities for:
• Predictive analytics
• Prescriptive insights
• AI/ML integration
• Translate business problems into data science use cases
• Ensure outputs are integrated into business workflows

11. Mentorship & Capability Building

• Mentor junior Product Owners and Data Analysts
• Drive best practices in:
• Data requirement definition
• KPI standardization
• Data product thinking
• Promote data literacy across business teams

What Differentiates This Role

• Owns data as a product, not just features
• Focuses on metrics, insights, and business value, not just UI/functionality
• Bridges business context with deep data understanding
• Drives enterprise-wide data consistency and trust