Job Description

About the Role

We are looking for a data-savvy Product Owner to own the horizontal data and AI capability that powers our reference/master data platform across all data domains. Where domain Process Owners own their individual processes, you will own what cuts across all of them: bringing new data sources and markets onto the platform, raising data quality and coverage, and embedding AI-driven enrichment, matching, and decisioning into how data is created and maintained.

As the Lead, you will set cross-domain data standards and hold cross-stream backlog priority so domain teams stay aligned to a single, coherent data model. This is a hands-on, technical role – fluent in SQL and data validation, comfortable shaping AI use cases from real operational problems, and able to translate them into build-ready user stories and test cases.

Key Responsibilities

Expand Data Sources & Market Coverage

- Own the roadmap for onboarding current data sources and feeds, and for expanding market/geographic coverage.
- Define source-onboarding standards – mapping, quality gates, and reconciliation – so new data lands consistently.
- Prioritize expansion by value, readiness, and data quality, in partnership with domain Process Owners and Program Managers.
- Coordinate market/data-source rollout waves – entry criteria, validation, and go/no-go inputs.

Own Data Quality & Cross-Domain Standards

- Define and govern cross-domain data-quality standards, coverage targets, and the shared data model all domains depend on.




- Build and run data-quality and reconciliation checks (SQL / data validation) across sources and domains.
- Resolve cross-domain data conflicts and hold priority so domains stay aligned to one coherent data model.

Drive AI Integration & Intelligence

- Shape AI/GenAI use cases from real operational problems – research, enrichment, auto-matching, translation, and coding recommendations.
- Define confidence-based routing and human-in-the-loop rules so high-confidence outputs flow automatically while low-confidence cases escalate for review.
- Partner with Data Science and Engineering to validate AI concepts through POCs and pilots, with clear success and continuation criteria.
- Ensure AI features meet governance, auditability, and operational-quality standards for enterprise deployment.

Write Stories, Test Cases & Validate (Technical Core)

- Translate data and AI needs into build-ready user stories and acceptance criteria.
- Author and validate test cases for data pipelines, enrichment, and AI outputs – including data-quality, drift, and edge-case scenarios.
- Verify results directly with SQL and data checks; own UAT sign-off for data and AI features.
- Maintain traceability from data/AI need → story → test → release.

Across Domains

- Hold cross-stream backlog priority and align domain Process Owners to shared data and AI standards.
- Facilitate trade-off decisions across domains, Solution Owners, and Program Managers on release and rollout planning.
- Provide leadership visibility on coverage, data quality, and AI adoption with clear, measurable outcomes.

📌 Product Owner – Data Intelligence & AI (Chennai)
🏢 Nielseniq
📍 Chennai