Data Engineer (AVP)
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Summary
Data Engineer (AVP) at OCBC Bank in Singapore builds, maintains, and improves batch and streaming data pipelines that feed enterprise data warehouse/lakehouse platforms and AI knowledge bases (RAG). Core stack includes Spark, SQL, Python, Airflow, Kafka/Flink, GCP/AWS with Docker/Kubernetes, and Terraform.
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
- Develop and maintain Airflow DAGs for scheduled and event‑driven workflows
- Follow orchestration best practices established by senior engineers
- 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
- 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
- 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
- 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
- Data Warehouse / Platform: exposure to Cloudera, BigQuery, Redshift, or similar
- Batch Processing: Spark, SQL, ETL, Python, Map/Reduce
- Streaming: familiarity with Flink or other real‑time processing engines (Good to have)
- CDC: exposure to Debezium, Confluent, Fivetran, or similar (Good to have)
- Data Ingestion: APIs, GA4, Pub/Sub, Kafka, Python pipelines
- Orchestration: Airflow or equivalents
- Cloud & Infrastructure: GCP or AWS; basic Docker/Kubernetes/Cloud Run experience
- DevOps / DataOps: exposure to CI/CD pipelines and Terraform
- Backend & Serving: Python, Flask, REST APIs; familiarity with Redis a plus
- Eagerness to learn real‑time / streaming architectures and low‑latency system design
- Basic exposure to LLM applications, RAG, or AI agent concepts is a plus, not required
- Good product mindset and willingness to treat data as a product, not just a project
- Strong communication skills and comfort collaborating with both technical and business stakeholders
- Competitive base salary and comprehensive benefits.
- Strong learning and development opportunities.
- Exposure to impactful, enterprise‑scale Data and AI initiatives across the OCBC Group.
- A collaborative environment that values innovation, craftsmanship, and continuous improvement.
Skills
- AI
- Airflow
- API
- Automation
- AWS
- BigQuery
- CI/CD
- Cloud
- Data Engineering
- Data Ingestion
- Data Pipelines
- Data Warehousing
- Debezium
- DevOps
- Docker
- Embeddings
- ETL
- Fivetran
- Flask
- Flink
- GCP
- Google Analytics
- Java
- Kafka
- Kubernetes
- Lakehouse
- LLM
- Python
- Redis
- Redshift
- REST
- Semantic Search
- Spark
- SQL
- Terraform
- Vector Databases