Senior Product Owner

Schneider Electric; Bengaluru; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

Schneider Electric is seeking a Senior Functional Product Owner (Data Analytics) in Bengaluru to lead end‑to‑end data product programs, bridging business, data engineering, and analytics teams. You will define KPI-driven roadmaps, ensure data quality, and drive adoption of analytics solutions across the enterprise.

You will collaborate with data engineers, BI developers, and scientists to deliver trusted insights, dashboards, and data services that support decision‑making and business outcomes.

Qualifications

• Bachelor's degree in a related field.
• Proven experience in data analytics and product ownership.
• Strong understanding of KPI frameworks and data modeling.

Responsibilities

• Own end-to-end data product delivery, from strategy to execution.
• Translate business questions into measurable KPIs and data models.
• Define data flows, pipelines, and semantic layers with data engineers and analysts.
• Drive backlog for data products, prioritizing value, quality, and scalability.
• Collaborate with data engineering, BI developers, and data scientists to deliver insights.

Skills

Data analytics

Stakeholder management

Agile / Scrum

Communication

Education

Bachelor's degree in a related field

Tools

Power BI

Tableau

Looker

SQL

Data warehousing

Azure

AWS

GCP

ETL/ELT

Data governance frameworks

Job description

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

• 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.

Required Skills

• 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, validations)
• Data warehousing concepts
• BI tools (Power BI, Tableau, Looker)
• Cloud data platforms (Azure, AWS, GCP)
• ETL/ELT pipelines
• Data governance frameworks

Success Metrics

• Data accuracy and reliability
• Adoption of analytics solutions
• Reduction in data defects
• Business impact and stakeholder trust

Endnotes

Note: The description above consolidates the role responsibilities and qualifications for clarity and structure. All content is presented as plain HTML with accessible headings and lists.