Data Quality Analyst

Key Responsibilities

Data Validation & Accuracy Checks

Validate data used in MIS reports, dashboards, and ad-hoc outputs before sharing with

stakeholders.

Cross-check numbers across multiple data sources such as CRM systems, internal

sheets, and reporting tools.

Identify inconsistencies, mismatches, duplicates, and missing data points.

Verify calculations, filters, logic, and aggregation methods used in reports.

Data Audits & Quality Monitoring

Conduct regular data audits across:

Sales data

Program and event data

CX and operational data

Identify anomalies and deviations from expected trends or values.

Maintain audit logs and track identified issues to closure.

Data Quality Controls & SOP Support

Support the creation and maintenance of data quality checklists and validation SOPs.

Ensure adherence to basic data rules such as mandatory fields, formats, and

consistency standards.

Flag recurring data quality issues and support preventive control implementation.

Issue Tracking & Coordination

Clearly document data issues with appropriate context and evidence.

Coordinate with MIS Executives, Analysts, and source teams to resolve data

discrepancies.

Follow up to ensure corrections are implemented and reflected in final outputs.

Documentation & Governance Support

Maintain and update basic documentation such as:

Data definitions

Validation checklists

Known issue trackers

Support senior team members in maintaining governance discipline across reports.

Support to BI & Insights Team

Act as the first-level quality checkpoint for all data outputs.

Reduce rework, escalations, and credibility risks caused by incorrect data.

Help build stakeholder confidence in the accuracy of reports shared by the data team.

Required Skills and Qualifications

Graduate degree in any discipline (preferred: Business, Analytics, IT, or related fields).

25 years of experience in MIS, reporting, data validation, or operational data roles.

Strong proficiency in Excel / Google Sheets.

Basic understanding of data structures and reporting logic.

Exposure to CRM or operational business data is preferred.

Basic SQL knowledge is a plus (not mandatory).

Attitude and Attributes

High attention to detail and accuracy.

Strong sense of ownership and accountability for data correctness.

Structured and process-driven approach to work.

Comfortable questioning data and flagging inconsistencies.

Ability to coordinate effectively with internal teams.

Willingness to learn and grow within the data quality and governance space.