Senior Product Manager

Location: Bengaluru, India

Job Requirements 1. Data Product Ownership Own and manage assigned data products throughout their lifecycle, from business need identification through implementation, adoption, enhancement, and ongoing support. elop and maintain a clear product vision, objectives, roadmap, priorities, and success criteria aligned with Customer business and data strategy. Serve as the primary point of coordination between business stakeholders, Data Analytics, Data Science, Technology teams and Data Engineering Team Ensure data products provide measurable business value and meet stakeholder expectations. 2. Business Requirements & Stakeholder Engagement Partner with business and technology stakeholders to understand business processes, reporting needs, analytical requirements, data challenges, and desired outcomes. Facilitate requirements-gathering sessions, workshops, and stakeholder discussions. Translate business requirements into clearly defined epics, features, user stories, functional requirements, and acceptance criteria. Identify and document data definitions, business rules, calculations, source-system requirements, dependencies, and expected outcomes. Obtain appropriate stakeholder alignment and approval for requirements and priorities. 3. Product Backlog & Prioritization Create, maintain, refine, and prioritize the data product backlog based on business value, strategic priorities, dependencies, risks, and delivery capacity. Ensure user stories and requirements are sufficiently defined and ready for development. Partner with Data Engineering and Analytics teams during backlog refinement, sprint planning, and delivery activities. Manage competing stakeholder priorities and facilitate appropriate prioritization decisions and escalation when required. 4. Data & Analytics Delivery Collaborate with Data Engineering teams on the delivery of data pipelines, data models, data marts, Gold-layer datasets, and other enterprise data products. Support solutions utilizing technologies including Databricks, Microsoft Azure Data Factory (ADF), Databricks Lakeflow, and Medallion Architecture (Bronze, Silver, and Gold), as applicable. Partner with Analytics teams to ensure curated data products appropriately support enterprise reporting, dashboards, operational analytics, and downstream consumption. Ensure business requirements are appropriately translated into scalable and supportable data solutions. 5. Data Quality, Governance & Business Definitions Define and document business data definitions, business rules, data quality expectations, and acceptance thresholds in partnership with business and technology stakeholders. Promote consistent definitions and usage of enterprise data across reporting and analytical products. Coordinate with Data Engineering and Data Governance teams to address data quality, lineage, ownership, classification, and metadata requirements. Ensure identified data quality issues are documented, prioritized, assigned, and tracked through resolution.