Data Engineer (AVP)

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

Job summary

OCBC is seeking a Data Engineer (AVP) to build and improve data pipelines powering an enterprise data warehouse and AI knowledge base within a banking environment.

You will work with senior engineers and the head of data engineering to transform structured and unstructured data into reliable datasets and services supporting risk management, customer engagement, fraud detection, and intelligent automation.

Qualifications

• Formal education in computer science with emphasis on data engineering.
• 5+ years of hands-on data engineering experience.
• Experience building pipelines on modern data architectures (DW, lakehouse).
• Familiarity with AI knowledge base concepts is a plus.
• Experience with RAG pipelines and vector databases is a plus.

Responsibilities

• Build and maintain batch and streaming data pipelines.
• Develop data models for reusable analytical datasets and reporting foundations.
• Operate data ingestion from APIs, GA4, and other sources.
• Develop Airflow DAGs for scheduled and event-driven workflows.
• Support real-time streaming pipelines and CDC pipelines.
• Collaborate with risk, marketing, and operations stakeholders.

Skills

Data engineering

Batch processing

Streaming pipelines

SQL

Python

Java

Airflow

Spark

Cloud & DevOps

Education

Bachelor's degree in computer science

Master's degree in computer science

Tools

Cloudera

BigQuery

Redshift

Fivetran

Debezium

Kafka

Pub/Sub

Redis

Terraform

Flask

Docker

Kubernetes

Job description

WHO WE ARE: As Singapore's longest established bank, we have been dedicated to enabling individuals and businesses to achieve their aspirations since 1932. How? By taking the time to truly understand people. From there, we provide support, services, solutions, and career paths that meet their individual needs and desires.

Today, we're on a journey of transformation. Leveraging technology and creativity to become a future-ready learning organisation. But for all that change, our strategic ambition is consistently clear and bold, which is to be Asia's leading financial services partner for a sustainable future.

We invite you to build the bank of the future. Innovate the way we deliver financial services. Work in friendly, supportive teams. Build lasting value in your community. Help people grow their assets, business, and investments. Take your learning as far as you can. Or simply enjoy a vibrant, future-ready career.

Your Opportunity Starts Here.

WHO WE ARE As Singapore's longest‑established bank, OCBC has supported individuals and businesses in achieving their aspirations since 1932. We are transforming into a future‑ready learning organization - leveraging technology and innovation while staying true to our ambition to be Asia's leading financial services partner for a sustainable future.

Join us to build the bank of the future, work in collaborative teams, and create lasting value for our customers and communities.

ROLE

We are seeking a Data Engineer (AVP) to build, maintain, and continuously improve data pipelines that power enterprise‑grade data warehouse, AI knowledge base within a banking environment. Working closely with senior engineers and the head of data engineering, you will help transform structured and unstructured data into reliable, reusable datasets and services that support use cases such as risk management, customer engagement, fraud detection, and intelligent automation. This is a hands‑on engineering role with strong opportunities to grow into streaming, real‑time, and AI‑driven data product work. This role reports to VP/ED, Data Engineering, Group data office.

KEY RESPONSIBILITIES

• Batch & Streaming Data Pipeline Development
  Build and maintain data pipelines feeding data warehouse / lakehouse platforms (e.g., Cloudera, AWS Redshift or GCP BigQuery)
  Implement data models to support reusable analytical datasets and reporting foundations
  Follow established SLAs and monitoring practices, and help troubleshoot pipeline issues
  Develop and maintain batch data processing jobs using Spark, SQL, Python or Java
  Support and contribute to real‑time streaming pipelines using Flink or similar tools, under senior guidance
  Assist in building and operating CDC pipelines (e.g., Debezium, Confluent or Fivetran)
  Build and maintain ingestion pipelines from APIs, GA4, and other data sources
  Implement messaging/streaming integrations using Pub/Sub and Kafka
  Write clean, well‑tested SQL and Python scripts and data ingestion and processing pipelines
• Orchestration & Automation
  Develop and maintain Airflow DAGs for scheduled and event‑driven workflows
  Follow orchestration best practices established by senior engineers
• Cloud Infrastructure & DevOps
  Deploy and support data workloads on Cloudera, GCP (Docker, Kubernetes, Cloud Run), AWS equivalent
  Contribute to and maintain CI/CD pipelines
  Use Terraform to provision and manage infrastructure under senior guidance
  Build and support REST APIs and backend services using Python / Flask
  Use Redis caching to meet performance requirements for data services
• AI Knowledge Base & RAG Support
  Assist in building and maintaining vector database pipelines and embedding generation jobs under senior guidance
  Support to deliver processing and chunking workflows that feed AI knowledge bases and RAG pipelines
  Build, test and monitor semantic search / retrieval quality for AI‑facing data layers
• Cross‑functional Collaboration
  Work closely with senior data engineers, AI teams, and business stakeholders (Risk, Marketing, Operations)
  Help translate business requirements into technical implementation tasks
  Participate in code reviews and contribute to a culture of continuous improvement

REQUIREMENTS

• Diploma, Bachelor's or Master's degree in computer science or a related field
• At least 5 years of experience in data engineering, data platforms, or related roles.
• Solid hands‑on experience in build data pipelines on modern data architectures including Data Warehouse, Lakehouse, and batch/real-time data processing systems.
• Working AI knowledge base concepts: vector databases, embeddings, and semantic search is a plus
• Hands‑on RAG pipeline components such as document chunking, embedding generation, and retrieval is a plus
• Good experience on building data layers that support LLM / AI agent use cases is a plus.
• Experience in banking or financial services is a plus

TECHNICAL STACK

Data Warehouse / Platform: exposure to Cloudera, BigQuery, Redshift, or similar