AWS Data Engineer

Tap Growth ai; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

Tap Growth ai is hiring an AWS Data Engineer in Singapore to design, build, and maintain robust data pipelines on AWS. You will collaborate with cross‑functional teams to solve complex data challenges and drive data-driven decisions.

You will work on end‑to‑end data lake/lakehouse architecture, ingestion pipelines with AWS services, and governance across data security and lifecycle policies, with a focus on performance and cost efficiency.

Qualifications

• 3–5 years in data engineering, data architecture, or cloud infra roles.
• Hands-on with core AWS data services: S3, Glue, Redshift, Lambda, Kinesis, Step Functions, EventBridge, AppFlow, 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).

Responsibilities

• Design end-to-end AWS Data Lake and Lakehouse architecture, including landing, transformed, and curated zones.
• Develop and deploy data ingestion pipelines using AWS Glue, Lambda, Step Functions, and API Gateway.
• Build and maintain data transformation workflows (batch and streaming) with AWS Glue and Redshift.
• Implement data security policies using Lake Formation, IAM, and Secrets Manager.
• Monitor platform health, troubleshoot pipeline issues, and optimise performance and cost on AWS.

Skills

SQL

Python/Scala

Data lake design

Batch & streaming

Data governance

Education

AWS Certified Data Engineer – Associate

AWS Certified Solutions Architect

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?