CavinKare - Customer Data Custodian Role

1) Job Title & Overview

Role: Customer Data Custodian (Customer Data Steward - Insights)

Role purpose: Own the quality, governance, accessibility, and compliance of customer data used for consumer insights, segmentation, journey mapping, and decision support. Ensure customer data is accurate, secure, well-documented, and analysis-ready, acting as the bridge between source systems and insight generation.

2) Key Responsibilities (including AI-related duties):

Data quality & stewardship:

- Maintain, update, enrich, and standardize customer master data across CRM/CDP/DWH and related platforms.

- Run routine data quality controls (completeness, validity, uniqueness, consistency, timeliness), detect duplicates, and drive remediation with stakeholders.

- Own the Customer Data Dictionary and business definitions (customer, household, contactability, consent status, identifiers).

Governance, privacy & access control:

- Implement and enforce role-based access, data handling protocols, retention policies, and audit readiness aligned to DPDP/GDPR and internal standards.

- Maintain consent and preference logic; ensure compliant usage of personal data in insights outputs and activation feeds.

- Partner with IT/InfoSec for security controls, incident response inputs, and periodic access reviews.

Pipelines, lineage & reliability:

- Support ETL/ELT validations during extraction, transformation, loading; reconcile row counts, key integrity, joins, and mapping rules.

- Track data lineage end-to-end (source - transforms - marts - dashboards/models).

- Monitor key feeds and jobs; raise issues early and coordinate fixes to protect business reporting accuracy.

Enablement for insights, segmentation & modeling:

- Provide clean, governed datasets for analysts; support segmentation datasets, journey data, and research sampling frames.

- Ensure dataset readiness for predictive modeling (feature integrity, missingness patterns, bias flags, drift checks).

AI-assisted productivity (expected):

- Use AI copilots to accelerate data profiling, anomaly detection, and documentation drafts-always with human verification before changes or release.

- Build repeatable "AI checklists" (e.g., duplicate detection suggestions, field definition drafts, transformation validation prompts).

3) Key Deliverables & Outputs:

- Customer Data Quality Scorecard (weekly/monthly): defect rates, duplicates, missingness, timeliness, SLA adherence.

- Clean-up & remediation plans with root-cause fixes and preventive controls.

- Customer Master Data Dictionary + Business Glossary (version-controlled).

- Access & Compliance Pack: access review logs, consent logic documentation, audit artifacts.

- Data Lineage Map for all customer datasets used in insights and dashboards.

- Validated analysis-ready datasets for segmentation, profiling, and journey analytics.

- AI-assisted monitoring rules (documented, governed) for anomaly alerts.

4) Required Technical Skills (AI/tools/analytics/automation):

- SQL (validation queries, reconciliation, profiling).

- Advanced Excel/Sheets (Power Query, pivots; data checks).

- CRM/CDP/DWH familiarity: Salesforce (preferred), CDP concepts (identity resolution), data warehouse tables/marts.

- Data quality techniques: de-duplication, standardization, referential integrity, sampling QA, exception handling.

- Knowledge of privacy basics: consent, purpose limitation, minimization, retention.

Bonus: ETL tools (Informatica/Talend/dbt/ADF), Python/R for data cleaning, basic Power BI/Tableau validation.

5) Domain & Industry Knowledge:

- Consumer/marketing data structures (customer, household, touchpoints, channels).

- Segmentation & journey analytics data needs.

- Common FMCG/retail/e-commerce data patterns (repeat purchase, channel attribution, contactability).