AI Data Engineer

UOB Kay Hian Pte Ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

UOB Kay Hian Pte Ltd is seeking a hands-on engineer to build and support an AI-driven Data Lake platform. You will own the enterprise data lake architecture, implement scalable pipelines in AWS and related tools, and ensure data quality, governance, and security.

Responsibilities include deploying AI Agents and GenAI-powered business solutions, integrating RAG architectures, and designing multi-agent workflows to automate processes for enhanced productivity.

Qualifications

• Hands-on data engineering with ETL/ELT pipelines.
• Strong data transformation, cleaning, and modeling skills.
• Proficient in SQL with large datasets.
• Familiar with Airflow, AWS Glue, S3, Redshift, Lambda.
• Knowledge of data quality, lineage, and reliability.

Responsibilities

• Own architecture, operation, and improvement of Data Lake and AI Platform.
• Design and maintain scalable data pipelines on cloud-native tech.
• Ensure data quality, governance, observability, security, and reliability.
• Establish standards for data ingestion, transformation, storage, and consumption.
• Design, build, and deploy AI Agents and GenAI-powered solutions.
• Develop Retrieval-Augmented Generation architectures leveraging data assets.
• Design multi-agent workflows to automate business processes.
• Evaluate emerging AI tech and opportunities to enhance capabilities.

Skills

ETL/ELT pipelines

SQL data modeling

Python programming

RESTful APIs

GenAI / LLMs

RAG architecture

Data quality & governance

System design thinking

Hybrid data/AI domain

Tools

Airflow

AWS Glue

S3

Redshift

Lambda

Docker

Kubernetes

OpenShift

MySQL

PostgreSQL

NoSQL databases

Job description

Role Overview

We are hiring a hands‑on engineer with strong capabilities in data pipelines and applied AI (GenAI) to build and support our AI-driven platform and Datalake platform.

Key Responsibilities

• Own the architecture, operation, and continuous improvement of the enterprise Data Lake and AI Platform.
• Design, implement, and maintain scalable data pipelines using AWS Glue, Apache Iceberg, Redshift, and related cloud-native technologies.
• Ensure data quality, governance, lineage, observability, security, and platform reliability.
• Establish standards and best practices for data ingestion, transformation, storage, and consumption.
• Design, build, and deploy AI Agents, AI Advisors, and GenAI-powered business solutions.
• Develop Retrieval-Augmented Generation (RAG)architectures leveraging enterprise knowledge and data assets.
• Design multi-agent workflows to automate business processes and improve user productivity.
• Evaluate emerging AI technologies and identify opportunities to enhance AI capabilities across the organization.

Core Skills (Must-Have)

1. Data Engineering Fundamentals

• Strong hands‑on experience in ETL/ELT pipeline development
• Proficient in data transformation, cleaning, and modeling
• Solid experience with SQL and working with large datasets
• Familiar with Airflow, AWS Glue, S3, Redshift, Lambda
• Understanding of data quality, lineage, and reliability concepts

2. Programming & Backend Development

• Strong proficiency in Python (preferred) or similar backend language
• Experience building RESTful APIs and backend services
• Ability to write clean, maintainable, production-grade code

3. GenAI / LLM Capabilities

• Hands‑on experience working with LLMs (e.g. OpenAI, Claude, or QWEN)
• Understanding of Retrieval-Augmented Generation (RAG) architecture
• Experience with embeddings, vector databases, and prompt orchestration
• Ability to connect enterprise data with LLMs in a secure and scalable way

4. Data Storage & Systems

• Experience with relational databases (e.g. MySQL, PostgreSQL)
• Familiarity with NoSQL / document stores
• Understanding of data lake / warehouse concepts

5. Deployment & Platform Skills

• Experience with Docker and containerization
• Basic familiarity with Kubernetes / AWS / OpenShift or similar platforms
• Understanding of CI/CD practices for backend or data applications

Good-to-Have Skills

• Experience with streaming data (Kafka or equivalent)
• Exposure to machine learning workflows
• Experience with API gateways, authentication, and security practices
• Familiarity with cloud platforms (AWS)
• Prior experience in financial services / trading systems

Key Attributes

• Able to operate as a hybrid engineer across data and AI domains
• Strong problem-solving and system design thinking
• Comfortable working in ambiguous, fast-moving environments
• Focus on delivering working solutions, not just prototypes

Scope (High-Level)

• Build and maintain data pipelines
• Enable AI/GenAI use cases (e.g. AI Advisor, Research Chatbot etc.)
• Integrate AI capabilities into applications and services