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Job Description
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
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