Data Engineer
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Summary
Data Engineer at R Systems in Singapore designing and running production-grade AWS data pipelines and platform services with Python, PySpark, and SQL. Day-to-day spans ETL/ELT and data lake work, AWS services (Glue, EMR, S3, Redshift, Lambda, Bedrock), CI/CD and IaC automation, Linux operations, and monitoring platform reliability and cost.
- Design, develop, and implement scalable data and platform solutions across AWS and
- Build and maintain robust data pipelines, platform services, and automation frameworks using Python, PySpark, SQL, and cloud-native technologies.
- Support and enhance enterprise capabilities including data lakes, metadata management, search platforms, and AI services.
- Develop and maintain CI/CD pipelines, automated testing frameworks, and deployment processes.
- Implement, integrate, and support technologies such as OpenSearch, Bedrock, AgentCore, MCP, and related platform services.
- Monitor and improve platform reliability, security, performance, observability, and cost
- Troubleshoot and resolve production issues while ensuring platform stability and
- operational excellence.
- Collaborate with cross-functional teams to drive platform modernization and
- 3-5+ years of hands-on experience building and supporting production-grade data pipelines using SQL, Python, and cloud technologies.
- Hands-on experience with AWS and/or hybrid cloud environments.
- Strong Linux/Unix administration and scripting experience with production support capabilities.
- Data Engineering: Strong understanding of ETL/ELT processes, Data Lake/Lakehouse architectures, and distributed data processing frameworks.
- Cloud & Platform Engineering: Experience with AWS Glue, EMR, S3, Redshift, Lambda, Bedrock, AgentCore and MCP.
- Platform Operations: Proficiency with Git, CI/CD pipelines, DevOps practices, Infrastructure as Code (IaC), and enterprise-scale platform support.