Data Engineer - Platform
Data Platform Engineer in Singapore building and operating an enterprise-scale Data Lake/data platform on AWS and hybrid environments. Day to day involves developing production data pipelines with Python, PySpark, and SQL, maintaining ETL/ELT workflows, CI/CD automation, and supporting AI/search services like OpenSearch and Amazon Bedrock.
- Compensation
- Not disclosed
- Location
- Singapore, Singapore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 3-5+ years hands-on experience building and supporting production-grade data pipelines using SQL, Python, and cloud technologies
- Practical experience with AWS and/or hybrid cloud environments
- Strong Linux/Unix administration, shell scripting, troubleshooting, and production support capabilities
- Solid understanding of data engineering principles, ETL/ELT processes, and distributed data processing
- AWS & data platforms: AWS Glue, EMR, S3, Redshift, Lambda
- Search & AI platforms: OpenSearch, Amazon Bedrock, AgentCore, MCP
- DevOps & automation: Git, CI/CD pipelines, Infrastructure as Code (IaC), automated testing, deployment automation
Nice to have
- Experience with enterprise Data Lake and Lakehouse platforms
- Knowledge of streaming and event-driven architectures
- Hands-on experience with OpenSearch administration, performance optimisation, and tuning
- Familiarity with monitoring, observability, and production support tools
- Understanding of data governance, security, access controls, and PII protection
- Experience in banking, insurance, or other financial services environments
Full description
We are looking for a motivated Data Platform Engineer to join Data Engineering team in Singapore. The team designs, operates, and enhances an enterprise-scale Data Lake and Data Platform supporting analytics, reporting, machine learning, enterprise search, and AI-driven solutions across cloud and on-premises environments.
The successful candidate will build and maintain scalable, secure, and reliable data pipelines and platform services, with a strong focus on AWS, Python, PySpark, SQL, automation, and production support.
Key Responsibilities
- Design, develop, and maintain scalable data platform solutions across AWS and hybrid/on-premises environments.
- Build, optimize, and support production-grade data pipelines using Python, PySpark, SQL, and cloud-native technologies.
- Implement and maintain ETL/ELT workflows, Data Lake/Lakehouse architectures, metadata management, search platforms, and AI services.
- Develop and maintain CI/CD pipelines, automated testing frameworks, deployment workflows, and infrastructure automation.
- Implement, integrate, and support AWS services and technologies such as OpenSearch, Amazon Bedrock, AgentCore, MCP, and related platform components.
- Monitor and optimize platform performance, reliability, security, observability, and cost efficiency.
- Troubleshoot production incidents, perform root-cause analysis, and ensure platform stability and operational excellence.
- Collaborate with data engineering, infrastructure, security, and application teams to drive platform modernisation and continuous improvement.
Mandatory Requirements
- 3-5+ years of hands-on experience building and supporting production-grade data pipelines using SQL, Python, and cloud technologies.
- Practical experience with AWS and/or hybrid cloud environments.
- Strong Linux/Unix administration, shell scripting, troubleshooting, and production support capabilities.
- Solid understanding of data engineering principles, ETL/ELT processes, and distributed data processing.
Technical Skills
- AWS & Data Platforms: AWS Glue, EMR, S3, Redshift, Lambda
- Search & AI Platforms: OpenSearch, Amazon Bedrock, AgentCore, MCP
- DevOps & Automation: Git, CI/CD pipelines, Infrastructure as Code (IaC), automated testing, deployment automation
Preferred Qualifications
- Experience with enterprise Data Lake and Lakehouse platforms.
- Knowledge of streaming and event-driven architectures.
- Hands-on experience with OpenSearch administration, performance optimisation, and tuning.
- Familiarity with monitoring, observability, and production support tools.