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
- Robust 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

- 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

- 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

- Domain Expertise (Preferred)

- Experience in one or more domains:
- Procurement / Supply Chain
- Finance
- Digital platforms
- Data & Analytics

- 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

- 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)

- 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

- 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

- 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

- 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

- 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

- 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

- 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

📌 Product Owner (Karnataka)
🏢 Schneider Electric
📍 Karnataka