Data Infrastructure Analyst

OCBC; Kuala Lumpur; On-site; MYR 120,000 - 180,000; Full time

Job summary

OCBC is seeking a data-focused professional in Kuala Lumpur to lead data development, migration and platform onboarding for credit risk datasets. The role requires strong SQL, Python and experience with data pipelines, validation, and governance.

You will collaborate with Risk, Finance, Technology and Operations to deliver reliable data for reporting and analytics. The position emphasizes automation opportunities, data quality, and ongoing improvements to data infrastructure to support risk

Qualifications

• Degree in a quantitative discipline; data-related degrees preferred.
• At least 5 years hands-on data management, analysis, testing or related roles.
• Strong SQL skills and data manipulation proficiency.
• Experience with Python for data processing or analytics is a plus.

Responsibilities

• Develop, migrate and onboard credit risk datasets across platforms.
• Validate data, perform testing, ensure accuracy and integrity of reports.
• Maintain data pipelines, data marts and processing routines for risk reporting.
• Identify automation opportunities for reports, data extraction and transforms.
• Support data quality governance, lineage and control documentation.
• Collaborate with Risk, Finance, Tech and Operations on data projects.

Skills

SQL proficiency

Python for data processing

Data analysis

Data management

Data testing

Education

Quantitative/technical degree

Tools

Hadoop

Cloudera

Job description

Job 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.

Job Requirements

• 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.