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 explicit 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.

📌 Sr Product Manager (Bengaluru)
🏢 Xoriant
📍 Bengaluru