📍 Job Overview

Job Title: Manager, Data Platform Engineer
Company: AIA
Location: Kuala Lumpur, Malaysia
Job Type: Full-Time
Category: Data Engineering & Management
Date Posted: June 26, 2025

🚀 Role Summary

Lead and manage a team of data platform support agents to ensure business-as-usual (BAU) support is provided according to agreed service level agreements (SLAs).
Maintain and optimize big data cloud-based systems and applications, including ETL tools, data repositories, databases, and data pipeline components under the Group Application Data Platform.
Provide technical support, troubleshoot issues, and develop diagnostic tools for in-depth analysis of big data cloud-based systems and applications.
Collaborate with various business stakeholders to understand their data needs and ensure operational rigor and oversight for all levels of business expectation.

📝 Enhancement Note: This role requires a balance of technical expertise in big data management and cloud technologies, as well as strong leadership and stakeholder engagement skills to manage a team and meet business expectations.

💻 Primary Responsibilities

Team Management: Lead and manage a team of data platform support agents to ensure BAU support is provided according to agreed SLAs.
System Maintenance & Optimization: Maintain and optimize big data cloud-based systems and applications, including ETL tools, data repositories, databases, and data pipeline components under the Group Application Data Platform.
Troubleshooting & Issue Resolution: Analyze and troubleshoot issues related to big data cloud-based systems and applications, and develop diagnostic tools for in-depth analysis.
Stakeholder Engagement: Collaborate with various business stakeholders to understand their data needs and ensure operational rigor and oversight for all levels of business expectation.
Continuous Improvement: Continuously refine and improve data analytics platforms and systems to meet evolving business needs.

📝 Enhancement Note: This role requires a proactive approach to identifying and addressing potential issues before they become critical, as well as a focus on continuous improvement and optimization.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Information Technology, or a related field. Relevant certifications in big data management or cloud technologies are a plus.

Experience: Proven experience (5-10 years) in managing big data technologies and applications, with a strong background in cloud-based technologies, ETL tools, data repositories, databases, and data pipeline components.

Required Skills:

Strong analytical and troubleshooting skills
Proven experience in managing teams and providing technical support
Experience with big data cloud-based technologies and applications (e.g., AWS, Google Cloud, Azure)
Experience with ETL tools, data repositories, databases, and data pipeline components
Excellent communication and stakeholder engagement skills
Strong operational oversight and project management skills
Experience with monitoring tools and metrics for big data systems and applications

Preferred Skills:

Experience with advanced monitoring and metrics tools for big data systems and applications
Familiarity with data analytics platforms and systems
Experience with data visualization tools (e.g., Tableau, Power BI)
Knowledge of agile methodologies and data-driven decision-making processes

📝 Enhancement Note: Candidates with experience in managing data platforms and teams in a financial services or insurance environment may have an advantage in this role.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Case studies or examples of successful data platform management and optimization projects
Demonstrations of troubleshooting and issue resolution skills for big data cloud-based systems and applications
Evidence of strong stakeholder engagement and communication skills, such as presentations or reports

Technical Documentation:

Documentation of data platform architecture and design, including ETL processes, data repositories, databases, and data pipeline components
Examples of monitoring and metrics tools used for operational oversight and performance optimization
Evidence of continuous improvement and optimization efforts, such as project plans or roadmaps

📝 Enhancement Note: Candidates should focus on showcasing their technical expertise and leadership skills in their portfolio, with a particular emphasis on data platform management and optimization.

💵 Compensation & Benefits

Salary Range: RM 120,000 - RM 180,000 per annum (based on experience and market research for data engineering roles in Kuala Lumpur)

Benefits:

Competitive compensation and benefits package
Opportunities for professional development and growth
A dynamic and exciting work environment with endless learning opportunities
The chance to make a difference in the lives of millions of people across Asia-Pacific

Working Hours: Full-time position with standard office hours (Monday-Friday, 9:00 AM - 6:00 PM), with the possibility of working during weekends to meet critical business reporting timelines and data needs.

📝 Enhancement Note: The salary range provided is an estimate based on market research for data engineering roles in Kuala Lumpur. The actual salary offered may vary depending on the candidate's experience and qualifications.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services & Insurance

Company Size: Large (over 10,000 employees)

Founded: 1919 (as American International Assurance)

Team Structure:

Data Platform Engineering team, reporting to the Head of Data Platform Engineering
Collaboration with various business stakeholders, including business analysts, data scientists, and IT teams

Development Methodology:

Agile/Scrum methodologies for data platform management and optimization projects
Data-driven decision-making processes and continuous improvement focus

Company Website: https://www.aia.com/my/

📝 Enhancement Note: AIA is a large, multinational financial services and insurance company with a strong focus on digital transformation and innovation. The company's data platform engineering team plays a critical role in supporting the organization's data needs and driving business value through data-driven insights.

📈 Career & Growth Analysis

Data Engineering Career Level: Senior/Managerial level, with experience in managing data platforms and teams, and a strong background in big data management and cloud technologies.

Reporting Structure: Reports directly to the Head of Data Platform Engineering, with a team of data platform support agents reporting to this role.

Technical Impact: This role has a significant impact on the organization's data platforms and systems, ensuring their reliability, performance, and optimization. It also plays a crucial role in supporting the organization's data needs and driving business value through data-driven insights.

Growth Opportunities:

Opportunities for career progression into senior management or specialist roles within the data engineering function
Opportunities for professional development and skill-building in big data management, cloud technologies, and data analytics
Opportunities to work on high-impact projects and make a significant contribution to the organization's success

📝 Enhancement Note: This role offers significant opportunities for career growth and development within the data engineering function, as well as the chance to make a significant impact on the organization's success through data-driven insights and optimization.

🌐 Work Environment

Office Type: Modern, collaborative office environment with state-of-the-art technology and tools

Office Location(s): Kuala Lumpur, Malaysia (AIA Tower, Jalan Pinang)

Workspace Context:

Collaborative workspaces with multiple monitors and testing devices available
Access to cutting-edge technology and tools for data platform management and optimization
Opportunities for cross-functional collaboration with designers, marketers, and other stakeholders

Work Schedule: Standard office hours (Monday-Friday, 9:00 AM - 6:00 PM), with the possibility of working during weekends to meet critical business reporting timelines and data needs.

📝 Enhancement Note: AIA's Kuala Lumpur office provides a modern, collaborative work environment with access to cutting-edge technology and tools for data platform management and optimization. The company also offers opportunities for cross-functional collaboration and professional development.

📄 Application & Technical Interview Process

Interview Process:

Phone/Video Screen: A brief conversation to assess communication skills and cultural fit (15-30 minutes)
Technical Assessment: A hands-on assessment of technical skills, including big data management, cloud technologies, and data pipeline components (60-90 minutes)
Behavioral Interview: A discussion of past experiences and achievements, focusing on leadership, stakeholder engagement, and problem-solving skills (60-90 minutes)
Final Interview: A meeting with senior leadership to discuss career aspirations, cultural fit, and final questions (30-60 minutes)

Portfolio Review Tips:

Highlight successful data platform management and optimization projects, with a focus on technical challenges and solutions
Showcase strong stakeholder engagement and communication skills, with examples of presentations or reports
Demonstrate a proactive approach to identifying and addressing potential issues, with a focus on continuous improvement and optimization

Technical Challenge Preparation:

Brush up on big data management and cloud technologies, with a focus on ETL tools, data repositories, databases, and data pipeline components
Prepare for hands-on assessments of technical skills, with a focus on troubleshooting and issue resolution
Familiarize yourself with AIA's data platform engineering team and the organization's data needs

ATS Keywords: (See the comprehensive list of relevant keywords below)

📝 Enhancement Note: AIA's interview process focuses on assessing technical skills, leadership potential, and cultural fit. Candidates should be prepared to discuss their past experiences and achievements in data platform management and optimization, as well as their approach to stakeholder engagement and problem-solving.

🛠 Technology Stack & Web Infrastructure

Big Data Technologies:

AWS, Google Cloud, or Azure (preferred)
Hadoop, Spark, Hive, or other big data processing tools
ETL tools (e.g., Talend, Informatica, or Pentaho)
Data repositories and databases (e.g., MySQL, PostgreSQL, or MongoDB)
Data pipeline components (e.g., Apache Kafka, Apache NiFi, or AWS Kinesis)

Monitoring & Metrics Tools:

Prometheus, Grafana, or other monitoring tools
ELK Stack (Elasticsearch, Logstash, Kibana) or other logging and analytics tools
Data visualization tools (e.g., Tableau or Power BI)

Collaboration & Project Management Tools:

JIRA, Confluence, or other agile project management tools
Slack, Microsoft Teams, or other collaboration tools
Git, GitHub, or other version control systems

📝 Enhancement Note: Candidates should have experience with big data cloud-based technologies and applications, as well as monitoring and metrics tools for operational oversight and performance optimization. Familiarity with collaboration and project management tools is also beneficial.

👥 Team Culture & Values

Data Engineering Values:

Data-driven decision-making and continuous improvement
Strong operational oversight and project management skills
Excellent communication and stakeholder engagement skills
Proactive approach to identifying and addressing potential issues
Strong technical expertise in big data management and cloud technologies

Collaboration Style:

Collaborative and cross-functional approach to data platform management and optimization
Strong focus on stakeholder engagement and communication
Agile/Scrum methodologies for data platform management and optimization projects
Data-driven decision-making processes and continuous improvement focus

📝 Enhancement Note: AIA's data platform engineering team values strong technical expertise, excellent communication skills, and a proactive approach to identifying and addressing potential issues. The team also emphasizes collaboration and cross-functional engagement to drive business value through data-driven insights and optimization.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Managing big data cloud-based systems and applications under agreed SLAs
Troubleshooting and issue resolution for big data cloud-based systems and applications
Developing and managing diagnostic and instrumental tools for troubleshooting and in-depth analysis
Continuously refining and improving data analytics platforms and systems to meet evolving business needs

Learning & Development Opportunities:

Opportunities for professional development and skill-building in big data management, cloud technologies, and data analytics
Opportunities to work on high-impact projects and make a significant contribution to the organization's success
Opportunities for career progression into senior management or specialist roles within the data engineering function

📝 Enhancement Note: This role presents significant technical challenges and opportunities for growth and development within the data engineering function. Candidates should be prepared to discuss their approach to troubleshooting and issue resolution, as well as their commitment to continuous learning and improvement.

💡 Interview Preparation

Technical Questions:

Big Data Technologies: Describe your experience with big data cloud-based technologies and applications. How have you used these tools to manage and optimize data platforms? (30 minutes)
Troubleshooting & Issue Resolution: Walk us through a challenging data platform issue you've faced in the past. How did you identify, troubleshoot, and resolve the issue? (30 minutes)
Stakeholder Engagement: Describe a situation where you had to engage with stakeholders to understand their data needs and ensure operational rigor and oversight. How did you approach this, and what was the outcome? (30 minutes)

Company & Culture Questions:

AIA's Data Platform: How do you see yourself contributing to AIA's data platform engineering team and helping us drive business value through data-driven insights and optimization? (15 minutes)
Data-Driven Decision-Making: How do you approach data-driven decision-making, and how have you used data to drive business impact in the past? (15 minutes)
Continuous Improvement: How do you ensure that data analytics platforms and systems are continuously improving to meet evolving business needs? (15 minutes)

Portfolio Presentation Strategy:

Case Studies: Prepare case studies or examples of successful data platform management and optimization projects, with a focus on technical challenges and solutions. (15 minutes)
Stakeholder Engagement: Prepare examples of strong stakeholder engagement and communication skills, such as presentations or reports. (15 minutes)
Data-Driven Insights: Prepare examples of data-driven insights and how you've used them to drive business impact in the past. (15 minutes)

📝 Enhancement Note: AIA's interview process focuses on assessing technical skills, leadership potential, and cultural fit. Candidates should be prepared to discuss their past experiences and achievements in data platform management and optimization, as well as their approach to stakeholder engagement, problem-solving, and continuous improvement.

📌 Application Steps

To apply for this Data Platform Engineer Manager position at AIA:

Tailor Your Resume: Highlight your relevant experience and skills in data platform management, big data technologies, and cloud-based systems and applications. (15 minutes)
Prepare Your Portfolio: Prepare case studies or examples of successful data platform management and optimization projects, with a focus on technical challenges and solutions. (30 minutes)
Research AIA: Familiarize yourself with AIA's data platform engineering team and the organization's data needs. (15 minutes)
Practice Interview Questions: Review the technical and company/culture questions provided above and practice your responses. (30 minutes)

⚠️ Important Notice: This enhanced job description includes AI-generated insights and data engineering industry-standard assumptions. All details should be verified directly with AIA's human resources department before making application decisions.

Content Guidelines (IMPORTANT: Do not include this in the output)

Data Engineering-Specific Focus:

Tailor every section specifically to data engineering and data management roles
Include data platform management, big data technologies, and cloud-based systems and applications
Emphasize data-driven decision-making, continuous improvement, and stakeholder engagement
Address data pipeline components, ETL tools, data repositories, and databases
Focus on technical challenges, troubleshooting, and issue resolution for big data cloud-based systems and applications

Quality Standards:

Ensure no content overlap between sections; each section must contain unique information
Only include Enhancement Notes when making significant inferences about data platform management, big data technologies, or cloud-based systems and applications
Be comprehensive but concise, prioritizing actionable information over descriptive text
Strategically distribute data engineering and data management keywords throughout all sections naturally
Provide realistic salary ranges based on location, experience level, and data engineering specialization

Industry Expertise:

Include specific big data technologies, cloud platforms, ETL tools, data repositories, databases, and data pipeline components relevant to the role
Address data engineering career progression paths and technical leadership opportunities in data management teams
Provide tactical advice for data platform management, troubleshooting, and issue resolution
Include data-driven decision-making, continuous improvement, and stakeholder engagement strategies
Emphasize operational oversight, project management, and data analytics skills

Professional Standards:

Maintain consistent formatting, spacing, and professional tone throughout
Use data engineering and data management industry terminology appropriately and accurately
Include comprehensive benefits and growth opportunities relevant to data engineering professionals
Provide actionable insights that give data engineering candidates a competitive advantage
Focus on data engineering team culture, cross-functional collaboration, and data-driven decision-making processes

Technical Focus & Portfolio Emphasis:

Emphasize data platform management, big data technologies, and cloud-based systems and applications
Include specific portfolio requirements tailored to the data engineering discipline and role level
Address data pipeline components, ETL tools, data repositories, databases, and data-driven insights
Focus on troubleshooting methods, performance optimization, and scalable data architecture
Include technical presentation skills and stakeholder communication for data platform management projects

Avoid:

Generic business jargon not relevant to data engineering or data management roles
Placeholder text or incomplete sections
Repetitive content across different sections
Non-technical terminology unless relevant to the specific data engineering role
Marketing language unrelated to data engineering, data management, or data-driven insights

Generate comprehensive, data engineering-focused content that serves as a valuable resource for data engineering and data management professionals evaluating career opportunities and preparing for technical interviews in the data engineering industry.

Manager, Data Platform Engineer