Data Engineer
6000 - 12000 SGDOVERSEA-CHINESE BANKING CORPORATION LIMITED
ROLE
We are seeking a Data Engineer (Manager) to support the development, maintenance, and improvement of data pipelines for enterprise data warehouse and AI knowledge base platforms within a banking environment. Working under the guidance of senior engineers, you will help transform structured and unstructured data into reliable, reusable datasets that support use cases such as risk management, customer engagement, fraud detection, and intelligent automation. This is a hands-on role with strong learning opportunities across cloud, streaming, and AI-enabled data engineering.
This role reports to Executive Director, Data Engineering, Group Data Office.
KEY RESPONSIBILITIES
Batch & Streaming Data Pipeline Development
Develop and support data pipelines feeding data lake/ data warehouse platforms, such as Cloudera, AWS Redshift, Snowflakes, under senior guidance.
Support the implementation of data models for reusable analytical datasets and reporting
Monitor data pipelines against established SLAs and assist in troubleshooting data quality and processing issues
Develop and maintain batch processing jobs using Spark, SQL, Python, or Java
Assist with real-time streaming pipelines using Flink or similar tools
Build and support ingestion pipelines from APIs, GA4, files, and other data sources
Support messaging and streaming integrations using Pub/Sub or Kafka
Write clean, tested, and maintainable SQL and Python code for data ingestion and processing
Orchestration & Automation
Develop and support Airflow DAGs (or similar tools) for scheduled and event-driven workflows
Follow established orchestration, coding, and operational standards
Cloud Infrastructure & DevOps
Support data workloads on Cloudera, or AWS, with exposure to Docker, Kubernetes ((or similar tools)), or Cloud Run
Use and support established CI/CD pipelines
Develop and support simple REST APIs and backend services using Python and Flask
Support Redis caching for data services where required
AI Knowledge Base & RAG Support
Assist in developing vector database pipelines and embedding generation jobs
Support document processing and chunking workflows for AI knowledge bases and RAG pipelines
Help test and monitor semantic search and retrieval quality for AI-facing data layers
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Cross‑functional Collaboration
Work with senior data engineers, AI teams, and business stakeholders across Risk, Finance, Marketing, and Operations
Assist in translating business requirements into clear technical tasks
Participate in code reviews, testing, documentation, and continuous improvement activities
REQUIREMENTS
Bachelor’s degree in computer science, information systems, engineering, or a related field
At least 2 years of relevant experience in data engineering, data platforms, software engineering, or related roles.
Hands-on experience building or supporting data pipelines using SQL and Python in a data warehouse, data lake, or cloud environment.
Basic knowledge of AI knowledge base concepts, such as vector databases, embeddings, or semantic search, is a plus
Exposure to RAG components, such as document chunking, embedding generation, or retrieval, is a plus
Exposure to data layers supporting LLM or AI agent use cases is a plus.
Experience or interest in banking and financial services is a plus
Technical Stack
Data Warehouse / Platform: basic experience with Cloudera, Redshift, or similar platforms
Batch Processing: working knowledge of SQL and Python; exposure to Spark, ETL, or MapReduce
Streaming: exposure to Flink or another real-time processing engine is good to have
Orchestration: basic experience with Airflow or equivalent tools
Cloud & Infrastructure: exposure to GCP or AWS; basic knowledge of Docker, or Cloud Run
DevOps / DataOps: basic exposure to CI/CD
Backend & Serving: Python, Flask, and REST APIs;
Additional Preferred Experience
Eagerness to learn data engineering, real-time streaming, cloud, and low-latency system design
Exposure to LLM applications, RAG, or AI agent concepts is welcome but not required
A quality-focused mindset and willingness to treat data as a reusable product
Good communication, teamwork, problem-solving, and willingness to learn
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