Senior Data Engineer

Position Title

Senior Data Engineer

Location

Singapore

Only Singaporean and Singapore PR

Role Overview

The Data Engineer will be responsible for designing, developing, optimizing, and supporting enterprise-scale data platforms that underpin the AML Strategic Uplift Program. The role will focus on Enterprise Data Warehouse (EDW), AML Data Repository (ADR), historical data frameworks, semantic data models, Universal Interfaces, data quality controls, lineage, and Data-as-a-Service enablement.

Key Responsibilities

Data Engineering & Development

• Design, develop, and maintain scalable data pipelines across Teradata, Hadoop, Iceberg, and Trino environments.
• Build and operationalize historical data frameworks supporting Slowly Changing Dimensions (SCD), snapshot processing, and large-scale history reconstruction.
• Develop ETL/ELT solutions for customer, payments, products, channels, compliance, and financial crime domains.
• Implement reusable metadata-driven ingestion and transformation frameworks.
• Optimize batch and near real-time processing performance.

Enterprise Data Modelling

• Develop and maintain enterprise data models supporting:
• Customer Party
• Payments
• Products
• Channels
• Financial Crime
• Risk & Compliance domains.
• Support harmonization and reconciliation of Payment, Product, and Channel transaction models.
• Build semantic data layers and reusable data products supporting Data-as-a-Service consumption.

Data Governance & Observability

• Implement data lineage, metadata management, auditability, and observability solutions.
• Develop reconciliation frameworks and automated data quality controls.
• Support enterprise metadata repositories, business glossaries, and data dictionaries.
• Implement operational monitoring and SLA reporting capabilities.

API & Universal Interface Development

• Develop and enhance Universal Interface Models (UIMs) and API-enabled data services.
• Enable secure and governed batch and API-based data delivery patterns.
• Support auditability, explainability, and regulatory traceability requirements.

DevOps & Automation

• Contribute to CI/CD implementation and automated deployment pipelines.
• Support automated testing and regression validation frameworks.
• Develop reusable engineering accelerators for productivity improvement.

Required Skills & Experience

• 5-10 years of Data Engineering experience.
• Proven experience in Banking, AML, Financial Crime, Risk, Regulatory Reporting, or Data Warehousing initiatives.
• Experience delivering enterprise-scale data transformation programs.

Technical Skills

Data Platforms

• Teradata
• Hadoop Ecosystem (Cloudera)
• Apache Iceberg
• Trino
• Enterprise Data Warehouse architectures.

Programming

• SQL (Advanced)
• Python
• Shell Scripting
• Spark/PySpark (preferred)

Data Engineering

• ETL/ELT Design
• Data Modelling
• Metadata Management
• Data Lineage
• Data Quality Frameworks
• SCD Type 1/2/3 implementations
• Data Reconciliation Techniques.

DevOps

• Git
• CI/CD Pipelines
• Release Management
• Automated Deployment Tools.

Preferred Skills

• AML / Financial Crime Data Domain expertise.
• Experience with Qlik Sense integration.
• Working knowledge of Data Mesh and Data Product concepts.
• Exposure to AI-assisted development frameworks.

Key Competencies

• Data problem-solving
• Performance optimization
• Analytical thinking
• Stakeholder collaboration
• Delivery focus