AWS Data Engineer

ITCAN PTE. LIMITED; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

ITCAN PTE. LIMITED in Singapore seeks a Senior Data Engineer/Architect to design and implement an end-to-end AWS Data Lake and Lakehouse platform.

You will define data architecture standards, build scalable ingestion pipelines, and ensure secure, governed data across hot, warm, and cold storage layers. The role requires hands-on AWS expertise (S3, Glue, Redshift, Lambda, Kinesis, Step Functions) and strong SQL/Python or Scala skills, with a focus on batch and stream processing and cost

Qualifications

• Minimum 3–5 years in data engineering, data architecture, or cloud infra roles.
• Strong SQL and at least one scripting language (Python or Scala).
• Experience designing Data Lake or Lakehouse architectures; governance and metadata management.

Responsibilities

• Design and architect end-to-end AWS Data Lake and Lakehouse solutions across Landing, Transformed, and Curated zones.
• Develop and deploy data ingestion pipelines using AWS Glue, Lambda, Step Functions, EventBridge, and API Gateway.
• Build data transformation workflows (batch & streaming) with AWS Glue and Redshift, including orchestration and monitoring.
• Maintain AWS Glue Data Catalogue with schema evolution tracking and metadata tagging.
• Configure data security policies using Lake Formation, IAM, and Secrets Manager; enforce granular access controls.
• Monitor platform health, troubleshoot pipeline failures, and optimize performance and cost on AWS.

Skills

SQL proficiency

Python/Scala scripting

Data architecture

AWS data services

Data governance

Batch & streaming processing

Tools

Amazon S3

AWS Glue

Amazon Redshift

AWS Lambda

Amazon Kinesis

AWS Step Functions

Amazon EventBridge

AWS AppFlow

AWS Lake Formation

Job description

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