AWS Data Engineer

Rapsys Technologies Pte Ltd.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Rapsys Technologies Pte Ltd. in Singapore is seeking an AWS Data Engineer to design, build, and maintain robust data pipelines on AWS. The role focuses on end-to-end data lake/lakehouse architectures, ingestion, transformation, and governance, with emphasis on security and scalable operations.

You will collaborate with cross-functional teams to deliver reliable data solutions, optimize costs, and ensure robust data quality across platforms in a fast-paced environment.

Qualifications

• Strong SQL and scripting skills (Python or Scala) required.
• Hands-on experience with core AWS data services and data lake architectures.
• Experience designing and implementing data pipelines, batch and streaming.

Responsibilities

• Design and architect end-to-end AWS Data Lake and Lakehouse solutions.
• Develop and deploy data ingestion pipelines and transformations.
• Implement security controls and governance for data assets.
• Monitor, optimize, and maintain data pipelines and catalogues.

Skills

SQL

Python/Scala

Tools

Amazon S3

AWS Glue

Amazon Redshift

AWS Lambda

Amazon Kinesis

AWS Step Functions

Amazon EventBridge

AWS AppFlow

AWS Lake Formation

Job description

We're Hiring: AWS Data Engineer!

We are looking for a skilled AWS Data Engineer to join our dynamic team and help design, build, and maintain robust data pipelines on AWS. The ideal candidate will have hands‑on experience in cloud‑based data engineering, a passion for solving complex data challenges, and the ability to collaborate effectively with cross‑functional teams.

Location: Singapore, Singapore
Work Mode: Work from Office
Role: AWS Data Engineer

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

Keen to advance your career in cloud data engineering?