Product Owner

Schneider Electric; Bengaluru; Sur place; INR 4 000 000 - 7 000 000; Plein temps

Résumé du poste

Schneider Electric is seeking a Senior Product Owner (Functional) – Data & Analytics to lead end-to-end data products, analytics solutions, and enterprise data capabilities. You will bridge business, data engineering, and analytics teams to deliver trusted data and actionable insights.

You will define data product strategy and roadmaps, translate business needs into KPIs, design data flows, own backlogs, and enable advanced analytics including AI/ML.

Qualifications

• 8–15+ years in data/product ownership or business analysis roles.
• Strong understanding of data modeling and KPI frameworks.
• Experience in Agile/Scrum environments.
• Strong stakeholder management and communication skills.

Responsabilités

• 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.

Connaissances

Experience: 8–15+ years

Data modeling

Agile/Scrum

Stakeholder management

Outils

SQL

Data warehousing

Cloud platforms

Description du poste

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
• Familiarity with cloud platforms (Azure, AWS, GCP)

Success Metrics

• Data accuracy and reliability
• Adoption of analytics solutions
• Business impact and stakeholder trust

Key Responsibilities

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

2. Data-Centric Requirement Management

• Lead discovery of data requirements across business domains
• KPIs and metrics
• Data models and datasets
• Analytical outputs
• Define:
• Metric logic (formulas, aggregations)
• Ensure alignment on "single source of truth" metrics across the organization
• 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:
• 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
• Own a multi-layered backlog, including:
• Data modeling (facts/dimensions)
• Prioritize work considering:
• Business value
• Dependency on upstream systems
• Data availability and quality
• Ensure all stories include:Acceptance criteria (data validation rules, thresholds)

5. Collaboration with Data & Engineering Teams

• Work closely with:
• 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:
• 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

• 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 limitations
• Delivery timelines

9. Value Realization & Performance Tracking

• Define and monitor success metrics for data products, such as:
• Adoption rate of dashboards
• 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
• Ensure outputs are integrated into business workflows

11. Mentorship & Capability Building

• Mentor junior Product Owners and Data Analysts
• Drive best practices in:
• 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