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Data Quality Analyst

Birbal AI
Abu Dhabi, UAE
fulltime
Entry
Yesterday
AnalystDataQuality
Free

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Overview

The Data Quality Specialist will be responsible for reviewing, validating, cleansing, and improving the quality of organizational data across enterprise systems, databases, reports, dashboards, and operational records.

The candidate will help ensure that data used for operations, reporting, dashboards, integrations, and decision-making is accurate, complete, consistent, valid, and reliable.

Key Responsibilities

Perform data profiling to identify missing, duplicate, invalid, inconsistent, or outdated records.

Review data across enterprise systems, CRM, databases, reports, dashboards, and Excel files.

Define and apply data validation rules for mandatory fields, formats, codes, reference values, and business rules.

Identify data quality issues and prepare clear issue logs for resolution.

Support data cleansing activities in coordination with business and technical teams.

Validate data updates, corrections, migrations, and system changes.

Reconcile data between source systems, reports, dashboards, and operational records.

Work with Data Engineers to resolve mapping issues, pipeline issues, and integration-related data errors.

Prepare data quality reports showing issue counts, root causes, trends, and resolution status.

Support report accuracy by validating source data and dashboard outputs.

Maintain data quality documentation, validation rules, cleansing logs, and issue trackers.

Ensure data confidentiality, privacy, governance, and compliance requirements are followed.

Required Skills

  • Good SQL knowledge for data checking, validation, and analysis.
  • Strong Excel skills, including filtering, formulas, lookup functions, and data comparison.
  • Experience in data profiling, data cleansing, data validation, and data reconciliation.
  • Understanding of data quality dimensions such as accuracy, completeness, consistency, uniqueness, validity, and timeliness.
  • Ability to identify duplicate records, missing values, wrong formats, and incorrect mappings.
  • Knowledge of databases, reports, dashboards, and enterprise application data.
  • Strong attention to detail, analytical thinking, and communication skills.

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