Executive, Data Architecture & Engineering

CTOS; Petaling Jaya; On-site; MYR 60,000 - 100,000; Full time

Job summary

CTOS is seeking an Executive for Data Architecture & Engineering to design and maintain cloud-based data pipelines, lakes, and warehouses. You will support ingestion, transformation, and storage using AWS services while building scalable data models for reporting and analytics.

The role emphasizes participation in POCs, productionization of solutions, and implementing automation for reliable data processing. Fresh graduates with strong tech skills may be considered.

Qualifications

• Bachelor’s degree in computer science, IT, data science, software engineering, or related field.
• Fresh graduates with strong technical skills or relevant projects may be considered.

Responsibilities

• Assist in designing, developing, and maintaining cloud-based data solutions (data lakes, data warehouses, databases, data pipelines).
• Support data ingestion, transformation, storage, and processing using AWS services.
• Develop and maintain data models to support reporting and analytics.
• Participate in POC initiatives and help productionize solutions.
• Assist in implementing automation to improve data engineering processes.
• Monitor data pipelines and jobs to ensure timely execution and reliability.
• Help implement logging, monitoring, alerting, and exception-handling mechanisms.

Skills

SQL

Python

AWS

ETL/ELT

Data modeling

Git

PySpark

Education

Bachelor’s degree in CS/IT/Data Science

Tools

AWS Glue

Talend

Amazon S3

Amazon Redshift

AWS Lambda

Power BI

Job description

The Executive, Data Architecture & Engineering will provide engineering and development support in delivering cloud-based ETL processes, data architecture designs, and automation. The role focuses on executing routine data tasks, assisting in pipeline development and to enable smooth day to day operations and process monitoring. This position is designed for early career professionals who are keen to build expertise in data engineering while contributing to CTOS’s cloud migration and reporting initiatives.

KEY RESPONSIBILITIES

• Assist in designing, developing, and maintaining cloud-based data solutions, including data lakes, data warehouses, databases, and data pipelines.
• Support the implementation of data ingestion, transformation, storage, and processing solutions using AWS services.
• Develop and maintain data models to support reporting, analytics, and downstream applications.
• Participate in Proof of Concept (POC) initiatives and support the transition of successful solutions into production.
• Assist in implementing automation to improve the efficiency and reliability of data engineering processes.

Pipeline Development & Operations

• Develop, maintain, and optimise ETL/ELT processes for structured and semi-structured data.
• Build data pipelines for scheduled, ad hoc, and bulk data ingestion and extraction based on business requirements.
• Monitor data pipelines and scheduled jobs to ensure successful and timely execution.
• Troubleshoot pipeline failures, data issues, and performance problems, escalating complex issues when necessary.
• Support operationalisation of automated and semi-automated data processes.
• Assist in implementing logging, monitoring, alerting, and exception-handling mechanisms.

Data Quality, Governance & Security

• Implement data quality controls and monitoring to ensure data accuracy, completeness, consistency, cleanliness, and reliability.
• Perform data validation and reconciliation between source and target systems.
• Follow established data governance, data classification, access control, and security standards.
• Ensure data processing and extraction activities comply with organisational security policies and regulatory requirements.
• Support proper handling and protection of sensitive and Personally Identifiable Information (PII).

Data Services & Reporting

• Execute scheduled and ad hoc data extraction requests in accordance with defined SLAs.
• Perform data extraction and integration from both internal and external data sources.
• Develop dashboards, reports, and data outputs based on stakeholder requirements.
• Assist business and analytics teams in accessing reliable and trusted datasets.
• Maintain technical documentation covering data pipelines, data mappings, workflows, operational procedures, and troubleshooting guides.
• Support the preparation and transformation of data for AI, Machine Learning, and Generative AI use cases.
• Have basic understanding of AI/ML concepts, including training data, features, models, inference, and model outputs.
• Explore the use of Generative AI and AI-assisted tools to improve data engineering productivity, documentation, data quality, and automation.
• Assist the team in conducting POCs involving AI/ML or Generative AI where required.
• Understand the importance of data privacy, security, responsible AI, and appropriate handling of sensitive data when using AI solutions.

WHAT DOES IT TAKE TO BE SUCCESSFUL?

Qualifications

• Bachelor’s degree in computer science, Information Technology, Data Science, Software Engineering, or a related field.
• Relevant cloud or data engineering certifications are an added advantage.

Work Experience

• 1–2 years of experience in Data Engineering, ETL/ELT, or a related field.
• Exposure to cloud platforms, preferably Amazon Web Services (AWS).
• Experience or exposure to data warehouse and/or data lake environments.
• Familiarity with dashboard and reporting tools.
• Fresh graduates with strong technical skills, internship experience, or relevant data engineering projects may also be considered.
• Good knowledge of SQL and Python.
• Exposure to PySpark or other distributed data processing technologies is an advantage.
• Understanding of relational databases, NoSQL databases, and data modelling concepts.
• Familiarity with ETL/ELT tools such as AWS Glue, Talend, or equivalent technologies.
• Exposure to AWS data services such as Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, Amazon RDS, or equivalent cloud services.
• Familiarity with BI and visualisation platforms such as Power BI, Amazon QuickSight, or equivalent tools.
• Understanding of data quality, data validation, data security, and access control principles.
• Ability to troubleshoot data, pipeline, and integration issues.
• Familiarity with Git/version control and basic software development practices is an advantage.
• Basic understanding of Artificial Intelligence, Machine Learning, and Generative AI concepts is an added advantage.

Interpersonal Skills

• Analytical and problem-solving mindset.
• Strong willingness to learn and adapt to new technologies.
• Good attention to detail, particularly when handling production data.
• Ability to work collaboratively within Data Engineering, Analytics, AI, and business teams.
• Ability to manage assigned tasks and meet agreed timelines and SLAs.
• Clear communication skills with the ability to explain technical issues to both technical and non-technical stakeholders.
• Demonstrates ownership, accountability, and continuous improvement mindset.