Data Quality Analyst

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Overview:

The Data Quality Analyst is responsible for supporting the Data Quality function by performing data quality checks, monitoring data quality results, analysing issues, maintaining issue logs, preparing reports, and following up with relevant stakeholders.

The role supports the implementation of data quality standards, SOPs, monitoring rules, dashboards, and issue management practices. The role helps ensure data quality issues are identified, documented, tracked, and escalated to the Data Quality Lead where required.

This role works with business users, source system teams, EDW teams, data lake teams, real-time data teams, reporting teams, and Data Quality Lead to support better data quality visibility and issue resolution.

Responsibilities:

1. Data Quality Monitoring and Reporting Support

• Perform data quality checks based on agreed rules, thresholds, and monitoring requirements.

• Monitor data quality results and identify exceptions, anomalies, data gaps, or unusual trends.

• Prepare data quality dashboards, reports, issue summaries, and supporting analysis.

• Maintain data quality monitoring records and ensure results are properly documented.

• Highlight recurring data quality issues, high-impact exceptions, and overdue actions to the Data Quality Lead.

• Support regular reporting to management, stakeholders, and data quality review forums.

2. Data Profiling, Validation and Analysis

• Perform data profiling, validation, reconciliation, and sample checking to support data quality assessment.

• Use SQL and data analysis techniques to investigate data quality exceptions.

• Compare data across source systems, EDW, data lake, real-time platforms, or reports where applicable.

• Analyse data patterns to identify possible root causes, business impact, and affected data areas.

• Prepare supporting evidence for data quality issues, including sample records, screenshots, query results, and analysis notes.

• Document assumptions, findings, known limitations, and follow-up actions clearly.

3. Data Quality Issue Tracking and Follow-Up

• Log data quality issues with clear description, impact, owner, priority, target date, and status.

• Track issue progress and follow up with assigned owners for updates and closure evidence.

• Maintain issue logs, action trackers, risk registers, and change request records where applicable.

• Support the Data Quality Lead in monitoring overdue issues, repeated issues, and high-risk items.

• Assist in preparing escalation materials for unresolved or critical data quality issues.

• Ensure closure updates and resolution evidence are properly recorded.

4. SOP, Framework and Governance Support

• Support the Data Quality Lead in maintaining data quality SOPs, standards, templates, and working procedures.

• Assist in preparing materials for data quality walkthroughs, alignment sessions, and review meetings.

• Apply agreed data quality processes consistently in monitoring, issue tracking, and reporting activities.

• Support updates to data quality documentation when there are changes to process, rules, or reporting requirements.

• Help ensure data quality artefacts are complete, updated, and easy to understand.

• Follow Bank policies, access control requirements, and data governance standards when handling data.

5. Stakeholder and Team Collaboration

• Work with business users, source system teams, EDW teams, data lake teams, real-time data teams, and reporting teams to clarify data quality issues.

• Support working-level discussions to understand data meaning, expected values, validation rules, and issue impact.

• Help stakeholders understand data quality findings, exceptions, and required follow-up actions.

• Escalate unclear ownership, delayed responses, conflicting explanations, or unresolved issues to the Data Quality Lead.

• Support cross-team collaboration while maintaining independence from delivery and remediation ownership.

• Share knowledge and contribute to consistent data quality practices within the team.

6. Continuous Improvement and Self-Development

• Identify opportunities to improve data quality checks, reports, templates, documentation, and issue tracking.

• Support automation of manual data quality checks and reporting where practical.

• Build knowledge in data quality principles, banking data, EDW, data lake, real-time data, and reporting environments.

• Continuously improve SQL, data analysis, profiling, validation, and issue investigation skills.

• Keep learning data quality tools, data governance practices, and industry good practices.

• Provide suggestions to improve the efficiency and effectiveness of data quality operations.

Managerial:

This role is not a people manager role. However, the Data Quality Analyst is expected to:

• Take ownership of assigned data quality monitoring and issue tracking tasks.

• Keep the Data Quality Lead updated on progress, risks, blockers, and support required.

• Maintain proper documentation and ensure assigned deliverables are completed on time.

• Support knowledge sharing within the team.

• Assist new joiners or junior team members where required.

Organizational:

• Support the Data Quality team in improving visibility and control over data quality issues.

• Help promote accountability for data quality across business, source system, platform, EDW, data lake, real-time data, and reporting teams.

• Support consistent use of data quality definitions, templates, issue logs, and reporting practices.

• Contribute to better trust in data used for reporting, analytics, operations, and decision-making.

• Follow Bank policies, access control requirements, data governance standards, and documentation practices.

• Support continuous improvement in data quality monitoring, issue tracking, reporting, and follow-up.

Skills & Experience We're Looking For:

• Minimum 3–5 years of experience in data quality, data analytics, data management, business intelligence, reporting, or data warehousing.

• Experience performing data analysis, data validation, reconciliation, issue tracking, or reporting.

• Experience using SQL for data checking, investigation, or reporting support.

• Experience working with business users, IT teams, or reporting teams to clarify data issues.

• Experience in banking or financial services is preferred.

• Exposure to EDW, data lake, real-time data, or reporting platforms is an advantage.

• Good understanding of data quality principles such as accuracy, completeness, consistency, timeliness, validity, and uniqueness.

• Good SQL and data analysis skills.

• Able to perform data profiling, validation, reconciliation, exception checking, and issue investigation.

• Able to prepare data quality reports, issue logs, analysis summaries, and supporting evidence.

• Familiarity with EDW, data lake, real-time data, reporting platforms, or analytical databases.

• Basic understanding of data governance, metadata, access control, and data security principles.

• Careful, structured, and detail-oriented in data checking and documentation.

• Able to explain data findings in clear and simple language.

• Able to follow up with stakeholders professionally and consistently.

• Good problem-solving, communication, and coordination skills.

• Able to work independently on assigned tasks while keeping the Data Quality Lead updated.

• Willing to learn new data platforms, tools, data quality practices, and banking data domains.

For more job opportunities, please go to HLB Careers: https://hlb.wd3.myworkdayjobs.com/HLBCareers/

We appreciate your application and will be in touch with shortlisted candidates regarding next steps.

Realise your full potential at Hong Leong Bank by applying now.