Senior Data Engineer

Infosys Consulting; Singapore; On-site; SGD 90,000 - 180,000; Full time

Job summary

Infosys Consulting is seeking a Data Engineer to design, build, and optimize enterprise-scale data platforms underpinning the AML Strategic Uplift Program. You will focus on EDW, ADR, historical data, semantic models, and Data-as-a-Service enablement.

The role covers data pipelines in Teradata, Hadoop, Iceberg, and Trino, plus governance, metadata, and observability to ensure regulatory traceability and reliable analytics for enterprise risk and compliance domains.

Qualifications

• 5-10 years of Data Engineering experience.
• Proven experience in Banking, AML, Financial Crime, Risk, or Data Warehousing initiatives.

Responsibilities

• Design, develop, and maintain scalable data pipelines across Teradata, Hadoop, Iceberg, and Trino environments.
• Build and operationalize historical data frameworks supporting SCDs, 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.
• Develop and maintain enterprise data models supporting Customer Party, Payments, Products, Channels, and Risk & Compliance.
• Support harmonization and reconciliation of transaction models; build semantic data layers and reusable data products for Data-as-a-Service.

Skills

SQL (Advanced)

Python

Shell Scripting

Spark/PySpark

ETL/ELT Design

Metadata Management

Data Quality Frameworks

SCD Type 1/2/3 implementations

Git

Release Management

Automated Deployment Tools

Qlik Sense integration

Data Mesh concepts

Tools

Hadoop ecosystem (Cloudera)

Trino

Teradata

Iceberg

API/Interface tooling

Job description

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

• 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.
• Develop and maintain enterprise data models supporting:
• Customer Party
• Payments
• Products
• Channels
• 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.

Technical Skills

Data Platforms

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

Programming

• SQL (Advanced)
• Python
• Shell Scripting
• Spark/PySpark (preferred)
• ETL/ELT Design
• Metadata Management
• Data Quality Frameworks
• SCD Type 1/2/3 implementations

DevOps

• Git
• Release Management
• Automated Deployment Tools.

Preferred Skills

• Experience with Qlik Sense integration.
• Working knowledge of Data Mesh and Data Product concepts.
• Exposure to AI-assisted development frameworks.