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
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)
5.6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
Experience- 8-15+ years in:
- Product Ownership / Business Analysis
- Data & Analytics / BI environments
- Proven experience delivering data-driven products at scale
Core Skills- Strong understanding of:
- Data modeling (facts, dimensions, star schema)
- KPIs, metrics, and analytical thinking
- Expertise in:
- Requirement gathering for data products
- Writing data-focused user stories and acceptance criteria
- Strong stakeholder management and communication
Technical Skills (Expected)- Working knowledge of:
- SQL (joins, aggregations, validations)
- Data warehousing concepts
- Familiarity with:
- Cloud data platforms (Azure, AWS, GCP)
- BI tools (Power BI, Tableau, Looker)
- Understanding of:
- ETL/ELT pipelines
- Data governance frameworks
Success Metrics- Accuracy and reliability of data products
- Adoption of analytics solutions by business users
- Reduction in data issues and rework
- Delivery of measurable business outcomes
- Stakeholder satisfaction and trust in data
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
Rewards designed for you
Our Total Rewards is our way of saying: We see you and we value you. It's more than just pay and benefits-it's a meaningful investment in you. It is designed to help you perform, grow, feel safe, and elevate your potential. The package helps you care for yourself and your family, plan your future, grow your skills and career, collaborate in an inclusive workplace, and contribute to your community. At Schneider Electric, we're here for what matters most to you. Discover more at our Career Page .
* Country-specific programs and initiatives may be available.
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€40 billion global revenue
+9% organic growth
150 000+ employees in 100+ countries
You must submit an online application to be considered for any position with us. This position will be posted until filled.
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Responsibilities
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
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)
5.6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
