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Job Description
Required Skills & Competencies
Advanced hands-on experience with SQL, data querying, data profiling, and data analysis
Strong practical understanding of data quality dimensions and how they appear in real datasets
Hands-on experience analyzing structured datasets across databases, spreadsheets, reporting extracts, data warehouses, or data lakes
Working knowledge of data validation, reconciliation, exception analysis, duplicate detection, pattern analysis, and data sampling techniques
Ability to interpret business rules and convert them into testable data conditions, validation logic, and expected outcomes
Ability to investigate data anomalies using data evidence, business context, system behavior, and process knowledge
Strong experience preparing data samples, issue evidence, exception lists, analytical summaries, and stakeholder-ready findings
Working knowledge of BI tools, reporting tools, profiling tools, data quality tools, or data catalog tools
Good understanding of source systems, transformations, data pipelines, reporting layers, and how data movement can affect quality
Ability to review analyst outputs and identify gaps in logic, evidence, completeness, or clarity
Strong Excel and data manipulation capability
Ability to explain technical data findings to non-technical business stakeholders
Good understanding of data governance, metadata, ownership, and stewardship concepts
Strong documentation skills for rule analysis, issue evidence, data observations, and validation results
Responsibilities
Perform advanced data profiling and data quality analysis
Analyze data issues across completeness, validity, consistency, uniqueness, integrity, accuracy, timeliness, and conformity
Investigate data anomalies, trends, outliers, exceptions, and recurring issue patterns
Interpret data quality issues using business process, system, and reporting context
Examine source data, transformation logic, business rules, and process behavior to understand issue drivers
Prepare analytical evidence, data samples, exception lists, and issue summaries
Support the definition and refinement of data quality rules, thresholds, and validation logic
Validate data quality dashboards, scorecards, reports, and exception outputs
Review outputs prepared by Data Quality Analysts for accuracy, completeness, and clarity
Support business discussions by explaining findings in practical, non-technical language
Maintain data quality analysis documentation, issue registers, rule documentation, and evidence packs
Support testing, retesting, and defect investigation where data validation is required
Identify recurring issue patterns and recommend practical improvement opportunities
Support knowledge transfer and capability building for junior analysts and client teams
The Senior Data Quality Analyst is a hands-on expert role. The role requires depth in SQL analysis, data profiling, validation, business curiosity, and the ability to move from raw data issues to clear, evidenced, and actionable findings.
Qualifications
Bachelor’s degree in Information Systems, Computer Science, Statistics, Mathematics, Business Analytics, Economics, Engineering, Management Information Systems, or a related discipline.
Arabic and English are mandatory.
4–7+ years of experience in data quality analysis, data profiling, SQL analysis, reporting analysis, data reconciliation, data migration, data integration, data governance, or business data analysis.
Experience working with complex datasets, business rules, exception analysis, reporting outputs, data validation, and technical/business stakeholders is preferred.
Preferred Certifications Include
DAMA CDMP Associate or Practitioner
Informatica Data Quality / IDMC certification where relevant
Microsoft, AWS, Google Cloud, Snowflake, Databricks, or equivalent data platform certification where relevant
SQL, analytics, BI, or data engineering certification where relevant
Business analysis or analytics certification where relevant
Required Skills & Competencies
Advanced hands-on experience with SQL, data querying, data profiling, and data analysis
Strong practical understanding of data quality dimensions and how they appear in real datasets
Hands-on experience analyzing structured datasets across databases, spreadsheets, reporting extracts, data warehouses, or data lakes
Working knowledge of data validation, reconciliation, exception analysis, duplicate detection, pattern analysis, and data sampling techniques
Ability to interpret business rules and convert them into testable data conditions, validation logic, and expected outcomes
Ability to investigate data anomalies using data evidence, business context, system behavior, and process knowledge
Strong experience preparing data samples, issue evidence, exception lists, analytical summaries, and stakeholder-ready findings
Working knowledge of BI tools, reporting tools, profiling tools, data quality tools, or data catalog tools
Good understanding of source systems, transformations, data pipelines, reporting layers, and how data movement can affect quality
Ability to review analyst outputs and identify gaps in logic, evidence, completeness, or clarity
Strong Excel and data manipulation capability
Ability to explain technical data findings to non-technical business stakeholders
Good understanding of data governance, metadata, ownership, and stewardship concepts
Strong documentation skills for rule analysis, issue evidence, data observations, and validation results
Responsibilities
Perform advanced data profiling and data quality analysis
Analyze data issues across completeness, validity, consistency, uniqueness, integrity, accuracy, timeliness, and conformity
Investigate data anomalies, trends, outliers, exceptions, and recurring issue patterns
Interpret data quality issues using business process, system, and reporting context
Examine source data, transformation logic, business rules, and process behavior to understand issue drivers
Prepare analytical evidence, data samples, exception lists, and issue summaries
Support the definition and refinement of data quality rules, thresholds, and validation logic
Validate data quality dashboards, scorecards, reports, and exception outputs
Review outputs prepared by Data Quality Analysts for accuracy, completeness, and clarity
Support business discussions by explaining findings in practical, non-technical language
Maintain data quality analysis documentation, issue registers, rule documentation, and evidence packs
Support testing, retesting, and defect investigation where data validation is required
Identify recurring issue patterns and recommend practical improvement opportunities
Support knowledge transfer and capability building for junior analysts and client teams
The Senior Data Quality Analyst is a hands-on expert role. The role requires depth in SQL analysis, data profiling, validation, business curiosity, and the ability to move from raw data issues to clear, evidenced, and actionable findings.
Qualifications
Bachelor’s degree in Information Systems, Computer Science, Statistics, Mathematics, Business Analytics, Economics, Engineering, Management Information Systems, or a related discipline.
Arabic and English are mandatory.
4–7+ years of experience in data quality analysis, data profiling, SQL analysis, reporting analysis, data reconciliation, data migration, data integration, data governance, or business data analysis.
Experience working with complex datasets, business rules, exception analysis, reporting outputs, data validation, and technical/business stakeholders is preferred.
Preferred Certifications Include
DAMA CDMP Associate or Practitioner
Informatica Data Quality / IDMC certification where relevant
Microsoft, AWS, Google Cloud, Snowflake, Databricks, or equivalent data platform certification where relevant
SQL, analytics, BI, or data engineering certification where relevant
Business analysis or analytics certification where relevant
Key Skills
Ranked by relevance
sql
cloud
excel
aws
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- Posted
- Jun 19, 2026
- Type
- Full-time
- Level
- Not Applicable
- Location
- Doha
- Company
- malomatia
Industries
IT Services
IT Consulting
Categories
Information Technology
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