Senior Data Engineer – Banking/Financial

UNISONEDGE CONSULTING PTE. LTD.; Singapore; On-site; SGD 120,000 - 170,000; Full time

Job summary

UNISONEDGE CONSULTING PTE. LTD. seeks a senior data engineer with 15+ years of hands-on experience in data engineering, data warehousing and enterprise ETL platforms.

You will lead large data engineering teams, design pipelines and oversee migration—working across Talend/DataStage/Informatica, Teradata, Hadoop and Spark. You will collaborate with banking technology teams, data management and business stakeholders to deliver robust data solutions, ensure quality and governance, and support

Qualifications

• 15+ years of hands-on experience in Data Engineering, Data Warehousing, ETL and enterprise data platforms.
• Strong expertise in Python, PySpark, SQL, Hadoop HDFS, Spark, Hive and Teradata.
• Proven experience designing and implementing large-scale data pipelines and ETL frameworks.
• Experience with enterprise ETL platforms such as Talend, DataStage or Informatica, including migration and optimisation.
• Experience with Teradata-to-Hadoop/Data Lake migration and modernisation.
• Experience with banking data platforms and regulatory/data initiatives.
• Hands-on data modelling, profiling, quality, lineage and large datasets.
• Proven technical leadership of large data engineering teams and delivery governance.
• Strong data architecture, CI/CD pipelines and batch processing using Autosys, Control-M, Jenkins and Git.
• Experience in production support and capacity planning in high-volume environments.
• Experience delivering regulatory or risk-data initiatives (AML, Fraud, MAS, BCBS239).

Responsibilities

• Provide technical leadership of large data engineering teams.
• Oversee solution design, code/design reviews and troubleshooting.
• Ensure delivery governance and production support adherence.
• Collaborate with Banking technology teams and business stakeholders.
• Drive data architecture and CI/CD pipelines.

Skills

Python

PySpark

SQL

Hadoop HDFS

Spark

Hive

Teradata

Data Modeling

ETL

Leadership

Education

Bachelor's degree in Computer Science or related field

Tools

Talend

DataStage

Informatica

Autosys

Control-M

Jenkins

Git

Teradata

Hadoop

Job description

15+ years of hands-on experience in Data Engineering, Data Warehousing, ETL and enterprise data platforms, preferably within Banking/Financial Services.

Strong hands-on expertise in Python, PySpark, SQL, Hadoop HDFS, Spark, Hive and Teradata; candidates without these core technologies will not be considered.

Proven experience designing and implementing large-scale data pipelines, ETL frameworks, data integration and batch processing solutions.

Extensive experience with at least one enterprise ETL platform such as Talend, DataStage or Informatica, including migration and optimisation.

Strong experience in Teradata-to-Hadoop/Data Lake migration, legacy platform modernisation, technology refresh and application decommissioning.

Experience working on enterprise Banking data platforms, preferably with Retail Banking, Cards, AML, Fraud, Regulatory Reporting or Data Warehouse environments.

Hands-on experience with data modelling, data profiling, data quality, data lineage, data transformation and large-volume structured/unstructured datasets.

Proven technical leadership of large data engineering teams, including solution design, code/design reviews, technical troubleshooting, delivery governance and production support.

Strong experience designing data architecture/frameworks, CI/CD pipelines, orchestration and batch processing using tools such as Autosys, Control-M, Jenkins and Git.

Experience with enterprise data migration, performance tuning, job optimisation, capacity planning and production/BAU support in high-volume banking environments.

Experience delivering regulatory or risk-data initiatives, such as AML, Fraud, MAS regulatory requirements, BCBS239, data quality or critical data element lineage.

Strong stakeholder management with Banking technology teams, Business Analysts, Infrastructure teams, Data Management teams and senior client stakeholders, with ownership of technical solution delivery.