Own End-to-End Commercial Data Delivery

Lead data delivery across discovery, sourcing, design, profiling, quality checks, UAT, release,

and adoption for commercial analytical products.

Ensure data is scalable, reliable, governed, and aligned to business and product priorities.

Drive Commercial Data Value

Partner with business and market teams to translate commercial problems into data requirements, analytics use cases, and measurable outcomes.

Demonstrating customer data in AI/ ML to support better business decision-making.

Lead KPI Ownership and Governance

Drive ownership, definition, validation, and governance of key commercial KPIs across markets and products.

Ensure consistent KPI logic, business rules, calculation methods, and usage across dashboards,

analytics, and AI use cases.

Relevant KPI areas include EPOS, Depletions, Retail Sales, Stock in Trade, Trade Investment,

outlet segmentation

Establish Data Management Practices

Implement robust data management practices across data quality, metadata, lineage, ownership,

controls, and issue management.

Define data quality rules, thresholds, remediation processes, and governance routines for commercial data.

Drive clear market ownership for source data quality, completeness, timeliness, and business validation.

Ensure Data Readiness for AI Use Cases

Ensure commercial data is fit for AI, advanced analytics, and automated decision-support use cases. Define what is required for data to be AI-ready, including clear ownership, trusted sources, consistent KPI definitions, metadata, lineage, data quality rules, usage controls, and documented business logic.

Support AI data governance by ensuring data inputs, assumptions, transformations, and outputs are traceable, explainable, and validated.

Lead UAT and Output Validation

Own data UAT planning and execution, including test scenarios, test cases, acceptance criteria,

reconciliation, defect management,



and business sign-off.

Validate datasets, KPI calculations, dashboards, analytical outputs, and AI-generated outputs against source data, business rules, and market expectations.

Lead Cross-Functional Collaboration

Act as the extension between business and technology teams, translating business needs into data requirements and technical delivery plans.

Influence stakeholders across markets, product, governance, engineering, architecture, finance,

and commercial teams to align on priorities, decisions, and ownership.

Qualifications and Experience Required

 Graduate, preferably with 8 years of Data experience

 Strong understanding of data management principles, including data quality, metadata,

lineage, ownership, governance, controls, and issue management.

 Experience implementing data quality frameworks, validation rules, KPI governance, and business data ownership models.

 In-depth understanding of customer data sources that fosters into RGM , Pricing, Promo solutions.

 Experience leading the build of AI-ready data foundations

 Ability to validate AI outputs using test cases, reconciliation, exception analysis, business reasonableness checks, and partners sign-off.

 Strong ability to structure work packages, define deliverables, assign ownership, track progress, manage risks, and raise issues.

 Experience leading data UAT, including test case creation, test execution, defect tracking,

retesting, and sign-off.

 Strong partner engagement and influencing skills across business, market, product,

governance, engineering, architecture, and finance teams.

 Ability to explain complex data topics clearly, align stakeholders, and drive decisions.

 Strong working knowledge of SQL, PySpark, Python, Power BI, and Excel.

 Valuable understanding of data platforms, data pipelines, data models, reporting layers,

source-to-target mapping, and analytical product architecture.

📌 Data Lead (Bengaluru)
🏢 Trigent Software
📍 Bengaluru