Data Quality Analyst

Starlink Qatar; Doha; On-site; QAR 120,000 - 190,000; Full time

Job summary

Starlink Qatar is seeking a Data Quality Analyst to join a large-scale eHealth programme in Doha, Qatar. The role focuses on operating data-quality controls to protect patient identity, provider data and downstream healthcare services, with emphasis on MPI and demographic-data integrity.

The analyst will work to ensure accurate patient matching, deduplication, and data integrity across the MPI, coordinating with healthcare providers and IT teams to investigate and resolve data issues.

Qualifications

• At least 1 year of experience in healthcare data or data quality.
• Understanding of patient demographics, patient matching, duplicate prevention and record linking/merging.
• Strong analytical, communication and documentation skills.
• Ability to perform repeatable data checks, investigate exceptions and coordinate corrections with healthcare providers and IT teams.

Skills

MPI and patient-demographic data

Matching and deduplication

SQL and data interrogation

Validation and exception management

Dashboards and trend analysis (PowerBI

ETL and data-pipeline awareness

Test cases and root-cause docs

Provider-data checks and stakeholder

Tools

Power BI

SAS

Job description

Location: Doha, Qatar

We are seeking a Data Quality Analyst (Analyst / Senior Analyst) to join a large-scale eHealth programme.

THE ROLE

Operate data-quality controls and investigations that protect patient identity, provider data and downstream healthcare services, with particular focus on Master Patient Index (MPI) and demographic-data integrity.

REQUIREMENTS

• At least 1 year of experience in healthcare data or data quality.
• Understanding of patient demographics, patient matching, duplicate prevention and record linking/merging.
• Strong analytical, communication and documentation skills.
• Ability to perform repeatable data checks, investigate exceptions and coordinate corrections with healthcare providers and IT teams.

KEY SKILLS

• MPI and patient-demographic data quality
• Matching, deduplication, survivorship and merge/unmerge concepts
• SQL and data interrogation
• Validation, cleansing, reconciliation and exception management
• Dashboards and trend analysis using Power BI, SAS or similar tools
• ETL and data-pipeline awareness
• Test cases, defect tracking, traceability and root-cause documentation
• Provider-data checks and stakeholder follow-up