AVP Data Engineer - Enterprise Data & AI Platforms

OCBC Bank; Singapore; On-site; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Competitive base salary

Learning & development opportunities

Flexible benefits

Job summary

OCBC Bank seeks a Data Engineer (AVP) to design, build, and optimize data pipelines powering an enterprise data warehouse and AI knowledge base. You will collaborate with senior engineers and stakeholders to transform structured and unstructured data into reliable datasets for risk management, fraud detection, and customer insights.

This hands-on role includes streaming architectures, real-time data processing, and contributing to AI-driven data products within a banking environment.

Qualifications

• Degree in computer science or related field.
• Minimum 5 years in data engineering or similar roles.
• Experience building data pipelines on modern architectures (DW/Lakehouse).
• Familiarity with AI knowledge bases, vector databases and RAG is a plus.

Responsibilities

• Batch & streaming data pipeline development for enterprise data needs.
• Build and maintain data pipelines to feed data warehouses/lakehouses (Cloudera, Redshift, BigQuery).
• Implement data models for reusable analytical datasets and reporting foundations.
• Develop Airflow DAGs and CI/CD pipelines; monitor SLAs and troubleshoot.
• Develop REST APIs/back-end services with Python; ensure data serving performance.

Skills

Batch & streaming pipelines

Data warehouse design

SQL

Python

Spark

Airflow

Cpp/Java

Kafka

Flink

ETL

Cloud platforms (GCP/AWS)

Docker/Kubernetes

Terraform

Redis

Vector databases / embeddings (RAG)

Education

Bachelor’s degree in computer science or related field

Tools

Cloudera

BigQuery

Redshift

GA4

Pub/Sub

Job description

OCBC Bank seeks a Data Engineer (AVP) to design, build, and optimize data pipelines powering an enterprise data warehouse and AI knowledge base. You will collaborate with senior engineers and stakeholders to transform structured and unstructured data into reliable datasets for risk management, fraud detection, and customer insights.

This hands-on role includes streaming architectures, real-time data processing, and contributing to AI-driven data products within a banking environment.