Sr AWS Data Engineer

ITC Infotech; Kuala Lumpur; On-site; MYR 180,000 - 260,000; Full time

Benefits offered by this job

On-site role

Client-facing exposure

Job summary

ITC Infotech in KL is seeking a Senior AWS Data Engineer for an on-site, client-facing role. You will lead architecture discussions, design end-to-end data pipelines, and translate business needs into scalable solutions for our clients.

Responsibilities include building ETL pipelines on AWS (Glue, PySpark, S3), using Step Functions and EventBridge for orchestration, and enforcing data quality and security best practices in a production environment.

Qualifications

• 7–10 years of hands-on AWS Data Engineering experience.
• Strong Client-Facing Experience: able to lead on-site conversations and present to diverse stakeholders.
• Architectural mindset with ability to plan end-to-end data implementations and defend design choices.
• Deep expertise in AWS Glue, PySpark, and data processing on S3.
• Production-level orchestration using AWS Step Functions and EventBridge.
• Terraform for Infrastructure as Code (IaC) proficiency.
• Strong SQL and data modeling skills.
• Experience building data validation, reconciliation, and quality frameworks.
• Solid understanding of cloud security, IAM, and AWS best practices.

Responsibilities

• Lead on-site customer workshops to understand business requirements and design end-to-end data flows.
• Articulate and justify architectural design decisions and data design patterns.
• Plan and sequence end-to-end data engineering implementations with clear roadmaps.
• Map theoretical knowledge to real-world client solutions.
• Design and develop metadata-driven ETL pipelines using AWS Glue and PySpark on S3.
• Implement complex data transformations (joins, aggregations, conditional logic, business rules).
• Optimize data pipelines for performance, scalability, and reliability.
• Implement data quality checks including integrity, completeness, and reconciliation.

Skills

AWS Data Engineering

Client-facing

Architectural mindset

AWS Glue

PySpark

S3 data processing

AWS Step Functions

EventBridge

Terraform

SQL

Data modeling

Data validation & quality

Cloud security & IAM

DynamoDB

CI/CD tools

Job description

We are seeking a highly skilled and consultative Senior AWS Data Engineer to join our team for a high-impact, client-facing engagement. In this on-site role, you will act as a technical leader, partnering directly with customers to design, plan, and execute end-to-end data pipelines.

The ideal candidate goes beyond coding—you must be able to confidently lead architectural discussions, justify design decisions, map theoretical concepts to real-world business problems, and clearly articulate implementation plans to client stakeholders.

Key Responsibilities:

• Lead on-site customer workshops to understand business requirements, capture transformation rules, and design end-to-end data flows.
• Clearly articulate and justify architectural design decisions, explaining which data design patterns best suit specific client use cases.
• Plan and sequence end-to-end data engineering implementations, defining clear roadmaps for ingestion, transformation, and access control.
• Map theoretical technical knowledge to practical, real-world solutions for the client.
• Design and develop robust, metadata-driven ETL pipelines using AWS Glue and PySpark on S3.
• Implement complex data transformations, including joins, aggregations, conditional logic, and business rule processing.
• Optimize data pipelines for performance, scalability, and reliability.
• Implement rigorous data quality checks, including integrity, completeness, and reconciliation validation.

Orchestration & Infrastructure

• Develop and manage workflow orchestration using AWS Step Functions and EventBridge for both event-driven and schedule-based execution.
• Provision and manage cloud infrastructure using Terraform (Infrastructure as Code).
• Deploy and configure AWS services (Glue, Lambda, DynamoDB) ensuring consistent, repeatable deployments aligned with DevOps practices.

Security, Governance & Operations

• Implement secure data access controls using AWS IAM and Lake Formation.
• Ensure compliance with data governance policies, managing encryption and access auditing.
• Set up monitoring, logging, and alerting mechanisms (e.g., SNS, CloudWatch, audit logs) to troubleshoot issues and drive continuous pipeline improvements.

Qualifications & Requirements:

Must-Have Skills & Experience:

• 7–10 years of hands-on experience in AWS Data Engineering.
• Strong Client-Facing Experience: Proven ability to work on-site, lead customer conversations, and present technical concepts to both technical and non-technical stakeholders.
• Architectural Mindset: Demonstrated ability to plan end-to-end data implementations and defend design choices.
• Deep expertise in AWS Glue, PySpark, and data processing on S3.
• Production-level experience with orchestration patterns using AWS Step Functions and EventBridge.
• Proficiency in Terraform for Infrastructure as Code (IaC).
• Strong SQL capabilities and data modeling skills.
• Experience building data validation, reconciliation, and quality frameworks.
• Solid understanding of cloud security, IAM, and AWS best practices.

Nice-to-Have Skills:

• Familiarity with AWS Lake Formation and data governance frameworks.
• Experience with DynamoDB or metadata-driven ETL architectures.
• Exposure to event-driven architectures.
• Knowledge of CI/CD tools and automated deployment pipelines.