WHO WE ARE:

As Singapore’s longest established bank, we have been dedicated to enabling individuals and businesses to achieve their aspirations since 1932. How? By taking the time to truly understand people. From there, we provide support, services, solutions, and career paths that meet their individual needs and desires.

 Today, we’re on a journey of transformation. Leveraging technology and creativity to become a future-ready learning organisation. But for all that change, our strategic ambition is consistently clear and bold, which is to be Asia’s leading financial services partner for a sustainable future.

 We invite you to build the bank of the future. Innovate the way we deliver financial services. Work in friendly, supportive teams. Build lasting value in your community. Help people grow their assets, business, and investments. Take your learning as far as you can. Or simply enjoy a vibrant, future-ready career.

Your Opportunity Starts Here.

Role Summary

The Data Infrastructure Analyst will support the development, migration, validation and maintenance of credit risk data assets used for reporting, analytics, modelling and regulatory submissions. The role requires hands-on data experience, strong problem-solving skills and the ability to work with business, risk, technology and Group stakeholders to ensure data is complete, accurate, well-controlled and delivered within required timelines.

A key part of this role is to improve operational efficiency through automation. The analyst is expected to identify automation opportunities, build or support end-to-end automation solutions where practical, and maintain appropriate manual review checkpoints where required to ensure the accuracy and completeness of reports before submission to regulators or senior stakeholders.

Job Roles & Responsibilities

1. Data Development, Migration and Platform Onboarding

• Work with data owners, technology teams and business users to develop, enhance and migrate credit risk datasets across existing and new platforms.
• Support onboarding of new platform data, including assessment of new data structures and formats such as JSON, semi-structured files and other non-traditional data layouts.
• Translate business and risk reporting requirements into data mapping, transformation logic and validation rules.
• Assist in designing migration approaches, reconciliation methods and cutover activities to ensure smooth transition with minimal disruption to reporting timelines.

2. Data Validation, Testing and Quality Assurance

• Perform detailed data validation, reconciliation and testing to ensure completeness, accuracy, consistency and integrity of data used for reporting, analytics and modelling.
• Develop test plans, test cases and expected results for data migration, system enhancement, new data ingestion and report automation activities.
• Investigate data discrepancies, identify root causes and coordinate resolution with relevant stakeholders.
• Maintain proper documentation of test outcomes, control checks, issue logs and sign-offs.

3. Data Infrastructure Maintenance and Data Processing

• Maintain reliable data pipelines, data marts and processing routines that support credit risk reporting, portfolio analysis and modelling requirements.
• Process structured, semi-structured and unstructured data using appropriate tools and technologies, including SQL, Python and big data/data lake environments where applicable.
• Identify relevant internal and external data sources and support efficient data ingestion, transformation and storage practices.
• Monitor scheduled jobs and data processes to ensure timely completion within agreed processing windows and resource constraints.

4. Automation and Continuous Improvement

• Identify automation opportunities across recurring reports, data extraction, data transformation, validation checks, reconciliations and operational monitoring.
• Develop or support end-to-end automation for existing tasks, including automation opportunities raised by other team members, where it is practical and well-controlled.
• Ensure automation solutions include adequate exception handling, audit trail, monitoring, documentation and user handover.
• Where full automation is not appropriate, clearly define and justify the manual checkpoint, particularly when an additional review layer is required to confirm that regulatory reports are accurate and in order before submission.
• Promote reusable scripts, standardised workflows and consistent control logic to reduce manual effort and operational risk.

5. Data Quality Management and Governance Support

• Perform regular data health checks and trend monitoring to detect data quality issues early.
• Track data quality action items, follow up with accountable parties and support sustainable remediation.
• Support documentation of data lineage, data definitions, transformation rules and control procedures.
• Contribute to a strong data control environment by ensuring issues, changes and exceptions are properly recorded and escalated.

6. Stakeholder Collaboration and Initiative Support

• Support local and Group initiatives related to data infrastructure, analytics, regulatory reporting and risk data transformation.
• Collaborate with cross-functional teams, including Risk, Finance, Technology, Operations and Group stakeholders, to deliver data-related projects.
• Communicate findings, risks, issues and progress clearly to both technical and non-technical stakeholders.
• Provide support during production incidents, reporting cycles, user queries and change implementation.

• Recognised degree in Statistics, Data Science, Computer Science, Information Systems, Engineering, Mathematics, Actuarial Science, Finance, Economics or other quantitative/data-related disciplines. Candidates with data-related degrees will have an added advantage.
• At least 5 years of hands-on working experience involving data management, data analysis, data testing, data infrastructure, data engineering, reporting automation or related data roles.
• Practical experience with SQL and data processing concepts, including data extraction, transformation, reconciliation and quality checks.
• Experience using Python for data processing, automation, validation or analytics is an added advantage.
• Exposure to data lake, Hadoop, Cloudera or similar big data ecosystems is an added advantage.

What we offer:

Competitive base salary. A suite of holistic, flexible benefits to suit every lifestyle. Community initiatives. Industry-leading learning and professional development opportunities. Your wellbeing, growth and aspirations are every bit as cared for as the needs of our customers.