AWS Data Engineer-AWS data services: Amazon S3, AWS Glue, Amazon Redshift,
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Summary
Designs, builds, and operates AWS data lake/lakehouse pipelines for a Singapore office-based team — ingesting data via Glue, Lambda, Step Functions, and AppFlow into S3 and Redshift, with day-to-day work on transformations, governance (Lake Formation/IAM), monitoring, and cost/performance optimisation.
We're Hiring: AWS Data Engineer!
We are searching for a skilled AWS Data Engineer to join our team in Singapore. If you have hands-on experience with Amazon S3, AWS Glue, and Amazon Redshift, and thrive in a fast-paced environment, we want to hear from you! Bring your expertise to help us design, build, and optimize data solutions that drive business insights.
Location:
Singapore, Singapore Work Mode:
Work from Office Role:
AWS Data Engineer What You'll Do: Key Responsibilities Architecture & Design • Design and architect the end-to-end AWS Data Lake and Lakehouse solution, including Landing Zone, Transformed Zone, and Curated/Consumption Zone layers • Define and govern data architecture standards, patterns, and best practices across the platform • Architect reusable data ingestion pipelines supporting REST APIs, JDBC databases, S3 file uploads, and SaaS connectors (e.g. Salesforce via AWS AppFlow) • Design data storage strategies including hot, warm, and cold storage tiers, encryption, and data lifecycle policies Development & Deployment • Develop and deploy data ingestion pipelines using AWS Glue, Lambda, Step Functions, EventBridge, and API Gateway • Build and maintain data transformation workflows (batch and stream processing) using AWS Glue and Amazon Redshift • Implement orchestration, monitoring, logging, and notification frameworks for pipeline operations • Develop and maintain the AWS Glue Data Catalogue, including schema evolution tracking and metadata tagging Security & Governance • Configure and enforce data security policies using AWS Lake Formation, IAM, and Secrets Manager • Implement granular access controls at database, table, and column levels • Ensure compliance with data classification, retention, and audit requirements • Support data quality frameworks and observability monitoring Maintenance & Operations • Monitor platform health, performance, and pipeline reliability • Troubleshoot and resolve data pipeline failures and data quality issues • Maintain documentation for architecture decisions, pipeline configurations, and operational runbooks • Continuously optimise platform performance and cost efficiency on AWS Requirements Essential • Minimum 3 to 5 years of experience in data engineering, data architecture, or cloud infrastructure roles • Hands-on expertise with core AWS data services: Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, Amazon Kinesis, AWS Step Functions, Amazon EventBridge, AWS AppFlow, AWS Lake Formation • Strong proficiency in SQL and at least one scripting language (Python or Scala) • Experience designing and implementing Data Lake or Lakehouse architectures • Solid understanding of data governance, data cataloguing, and metadata management • Experience with batch and streaming data processing patterns • AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect certification (or equivalent) Preferred • Experience integrating with Tableau or similar BI visualisation tools via Amazon Redshift or S3 • Familiarity with MLOps frameworks and AI/ML model deployment on AWS SageMaker • Experience with Salesforce data integration using AWS AppFlow • Knowledge of Change Data Capture (CDC) and incremental data load patterns • Prior experience in a government or public sector data environment
Eager to elevate your career Apply now and be part of our innovative journey!
Singapore, Singapore Work Mode:
Work from Office Role:
AWS Data Engineer What You'll Do: Key Responsibilities Architecture & Design • Design and architect the end-to-end AWS Data Lake and Lakehouse solution, including Landing Zone, Transformed Zone, and Curated/Consumption Zone layers • Define and govern data architecture standards, patterns, and best practices across the platform • Architect reusable data ingestion pipelines supporting REST APIs, JDBC databases, S3 file uploads, and SaaS connectors (e.g. Salesforce via AWS AppFlow) • Design data storage strategies including hot, warm, and cold storage tiers, encryption, and data lifecycle policies Development & Deployment • Develop and deploy data ingestion pipelines using AWS Glue, Lambda, Step Functions, EventBridge, and API Gateway • Build and maintain data transformation workflows (batch and stream processing) using AWS Glue and Amazon Redshift • Implement orchestration, monitoring, logging, and notification frameworks for pipeline operations • Develop and maintain the AWS Glue Data Catalogue, including schema evolution tracking and metadata tagging Security & Governance • Configure and enforce data security policies using AWS Lake Formation, IAM, and Secrets Manager • Implement granular access controls at database, table, and column levels • Ensure compliance with data classification, retention, and audit requirements • Support data quality frameworks and observability monitoring Maintenance & Operations • Monitor platform health, performance, and pipeline reliability • Troubleshoot and resolve data pipeline failures and data quality issues • Maintain documentation for architecture decisions, pipeline configurations, and operational runbooks • Continuously optimise platform performance and cost efficiency on AWS Requirements Essential • Minimum 3 to 5 years of experience in data engineering, data architecture, or cloud infrastructure roles • Hands-on expertise with core AWS data services: Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, Amazon Kinesis, AWS Step Functions, Amazon EventBridge, AWS AppFlow, AWS Lake Formation • Strong proficiency in SQL and at least one scripting language (Python or Scala) • Experience designing and implementing Data Lake or Lakehouse architectures • Solid understanding of data governance, data cataloguing, and metadata management • Experience with batch and streaming data processing patterns • AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect certification (or equivalent) Preferred • Experience integrating with Tableau or similar BI visualisation tools via Amazon Redshift or S3 • Familiarity with MLOps frameworks and AI/ML model deployment on AWS SageMaker • Experience with Salesforce data integration using AWS AppFlow • Knowledge of Change Data Capture (CDC) and incremental data load patterns • Prior experience in a government or public sector data environment
Eager to elevate your career Apply now and be part of our innovative journey!