About the role
The core responsibilities for the job include the following:
Data Quality and Validation:
- Perform profiling, rule checks, and data health assessments for Customer and Sales datasets.
- Identify data gaps, inconsistencies, duplicates, and integrity issues; propose corrective actions.
- Work with data owners and stewards to align on standards and definitions.
- Track and report data quality KPIs.
Ground Truth and AI Enablement:
- Collaborate with SMEs to gather ground truth for model training, labeling, and evaluation.
- Validate datasets to ensure accurate representation of Customer and Sales processes.
- Support feature validation, input-output checks, and pre-UAT reviews.
Requirements and Analysis:
- Capture data, process, and reporting requirements for AI use cases.
- Document data requirements, transformation rules, acceptance criteria, and test scenarios.
- Map data flows across CRM, Sales, and Customer Master systems.
Compliance and Risk:
- Ensure data minimization and proper handling of personal and confidential data.
- Maintain risks, issues, and mitigation plans.
UAT and Delivery Support:
- Prepare test cases tied to data rules and model requirements.
- Execute UAT with focus on data accuracy; identify defects and drive closure.
- Validate outputs against business logic and acceptance criteria.
- Support deployment readiness and handover documentation.
Communication and Documentation:
- Provide clear status updates, risks, and insights.
- Maintain data dictionaries, quality rules, lineage, and test assets.
- Update AI and data compliance trackers.
Requirements:
- Strong experience working on data quality assessment and analysis/reporting
- Hands-on big data handling, profiling, and quality experience.
- Proven Data project management: planning, execution, defect management, and sign-off.
- Excellent stakeholder management, facilitation, and communication skills.
- Comfortable working in agile cross-functional teams.