Senior Data Engineer – Banking/Financial

REGTECH INSIGHT PTE. LTD.; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

REGTECH INSIGHT PTE. LTD.

seeks a senior Data Engineering Lead to design and implement large-scale data solutions in banking contexts, driving platform modernization, migration, and regulatory initiatives while leading technical teams and coordinating with stakeholders to ensure robust data delivery. You will develop data pipelines using Python, PySpark, SQL, Hadoop/HDFS, Spark, Hive, and Teradata, and oversee ETL platforms like Talend, DataStage, and Informatica, ensuring scalable production

Qualifications

• Hands-on expertise with Python, PySpark, SQL, Hadoop/HDFS, Spark, Hive and Teradata.
• Experience with enterprise ETL platforms (Talend, DataStage, Informatica).
• Proven technical leadership of large data engineering teams.
• Experience in Teradata-to-Hadoop/Data Lake migration and legacy modernization.
• Experience delivering regulatory/risk-data initiatives (AML, MAS, BCBS239).

Responsibilities

• Design and implement large-scale data pipelines and ETL/frameworks.
• Develop data integration using Python, PySpark, SQL, Hadoop, Spark, Hive, Teradata.
• Lead migration to data lake, modernize legacy platforms, and refresh tech.
• Manage enterprise ETL platforms and perform migration and tuning.
• Provide technical leadership, reviews, governance, and production support.
• Design data architectures, CI/CD pipelines and orchestrations (Autosys/Control-M/Jenkins/Git).
• Oversee data migration, capacity planning and production/BAU in banking environments.
• Deliver AML, Fraud, MAS regulatory data initiatives and data lineage compliance.

Skills

Python

PySpark

SQL

Hadoop

Spark

Hive

Teradata

Data Lake

Data Modeling

Leadership

Tools

Talend

DataStage

Informatica

Autosys

Control-M

Jenkins

Git

Job description

Job Summary

Lead the design and implementation of large-scale data engineering solutions within banking environments. Drive data platform modernization, migration, and regulatory initiatives while managing technical teams and collaborating with stakeholders to ensure robust, high-quality data delivery.

Responsibilities

• Design and implement large-scale data pipelines, ETL frameworks, and batch processing solutions to support enterprise data platforms.
• Develop and optimize data integration processes using Python, PySpark, SQL, Hadoop HDFS, Spark, Hive, and Teradata technologies.
• Lead Teradata-to-Hadoop/Data Lake migration projects, legacy platform modernization, technology refresh, and application decommissioning efforts.
• Manage and optimize enterprise ETL platforms such as Talend, DataStage, or Informatica, including migration and performance tuning.
• Apply data modeling, profiling, quality assurance, lineage tracking, and transformation techniques on large structured and unstructured datasets.
• Provide technical leadership to large data engineering teams through solution design, code and design reviews, troubleshooting, delivery governance, and production support.
• Design data architecture frameworks, CI/CD pipelines, and orchestration workflows using Autosys, Control-M, Jenkins, and Git.
• Oversee enterprise data migration, job optimization, capacity planning, and production/BAU support in high-volume banking environments.
• Deliver regulatory and risk-data initiatives aligned with AML, Fraud, MAS regulations, BCBS239, data quality, and critical data element lineage requirements.
• Manage stakeholder relationships with banking technology teams, business analysts, infrastructure, data management, and senior clients to ensure successful technical solution delivery.

Required competencies and certifications

• Hands-on expertise in Python, PySpark, SQL, Hadoop HDFS, Spark, Hive, and Teradata technologies.
• Experience with enterprise ETL platforms such as Talend, DataStage, or Informatica.
• Proven technical leadership of large data engineering teams.
• Experience in Teradata-to-Hadoop/Data Lake migration and legacy platform modernization.
• Experience delivering regulatory or risk-data initiatives including AML, Fraud, MAS regulatory requirements, BCBS239, and data quality.

Preferred competencies and qualifications

• Experience within Banking/Financial Services, especially Retail Banking, Cards, AML, Fraud, Regulatory Reporting, or Data Warehouse environments.
• Strong stakeholder management skills with cross-functional teams and senior client stakeholders.
• Experience designing CI/CD pipelines and orchestration using Autosys, Control-M, Jenkins, and Git.
• Experience with performance tuning, job optimization, capacity planning, and production/BAU support in high-volume banking environments.