Data Quality Analyst

JOB DESCRIPTION

The Data Quality Analyst will support the monitoring, analysis, and improvement of enterprise data quality across the bank. The role focuses on executing data quality checks, identifying issues, and supporting resolution, ensuring reliable data for analytics, regulatory reporting, and AI initiatives.

This role plays a key part in building a culture of data trust and accountability across business and technology teams.

Key Responsibilities

• Execute data quality checks and validations as per defined rules and thresholds.
• Perform data profiling and reconciliation across source and downstream datasets.
• Identify data quality issues related to accuracy, completeness, consistency, timeliness, and validity.
• Support logging, tracking, and closure of data quality issues in coordination with senior team members.
• Assist in root cause analysis by analysing data patterns and anomalies.
• Work closely with data engineering teams to understand data pipelines and transformations.
• Support preparation of data quality dashboards, reports, and scorecards.
• Maintain documentation for data quality rules, issues, and resolutions.
• Follow defined data quality standards, SOPs, and governance processes.
• Support audits, internal reviews, and regulatory requests as required.

Qualifications & Experience

• Bachelor's degree in Engineering, Statistics, Mathematics, Computer Science, or related field.
• 1.5–6 years of experience in data analysis, data quality, or related roles.
• Exposure to enterprise data environments is desirable.
• Experience in banking or financial services is a plus.

Technical Skills

• Strong SQL and Python skills for data analysis and validation.
• Understanding of data quality dimensions and validation techniques.
• Basic knowledge of data warehousing and ETL/ELT concepts.
• Ability to perform data reconciliation and anomaly identification.
• Familiarity with data quality documentation and reporting.
• Awareness of data security, access control, and compliance requirements.
• Willingness to learn banking data domains and regulatory expectations.
• Basic exposure to analytics or AI data requirements is an advantage.