Data Engineer (Mgr)

OCBC; Singapore; Vor Ort; SGD 120.000 - 180.000; Vollzeit

Zusammenfassung

OCBC is seeking a Data Engineer (Manager) to design and maintain data pipelines for enterprise data warehouse and AI knowledge bases in a banking context. You will transform structured and unstructured data into reliable datasets supporting risk, customer engagement, and fraud use cases.

The role offers hands-on opportunities across cloud, streaming, and AI-enabled data engineering under the guidance of senior engineers.

Qualifikationen

• Bachelor’s degree in computer science, information systems, engineering, or 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

Aufgaben

• Develop and support data pipelines feeding data lake/data warehouse platforms.
• Support the implementation of data models for reusable analytical datasets and reporting.
• Monitor data pipelines against SLAs and assist in troubleshooting data quality 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 SQL and Python code for data ingestion and processing.
• Collaborate with cross-functional teams to translate business requirements into technical tasks

Kenntnisse

Data pipelines

SQL

Python

Batch processing

Streaming processing

Ausbildung

Bachelor's degree in CS/IS/Engineering

Tools

Cloudera

Redshift

Snowflake

Spark

Flink

Airflow

Docker

Kubernetes

Cloud Run

Jobbeschreibung

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.

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

What we offer:

Competitive base salary. A suite of holistic, flexible benefits to suit every lifestyle. Community initiatives. Industry‑leading learning and professional development opportunities. Your wellbeing, growth and aspirations are every bit as cared for as the needs of our customers.