Data and AI Ops Engineer

Jobstreet Malaysia; Kuala Lumpur; On-site; MYR 100,000 - 150,000; Full time

Job summary

A financial services provider in Kuala Lumpur is looking for a Data & AI Ops Engineer to design and maintain scalable data pipelines. The role requires expertise in data architecture, API integration, and cloud platforms. Ideal candidates should have a Bachelor's in a related field or equivalent experience with a strong foundation in data engineering and operational support. Excellent problem-solving and communication skills are essential. A competitive salary and opportunities for professional growth are offered.

Qualifications

• Minimum 6 years of hands-on experience in data engineering or data integration.
• At least 3 years in designing and supporting enterprise-scale data pipelines.
• Minimum 8 years of practical experience with a diploma.

Responsibilities

• Design and maintain scalable data pipelines for analytics and reporting.
• Ensure data quality and reliability through validation and transformation.
• Collaborate with teams to define data requirements and solutions.
• Optimize data storage and performance across cloud platforms.
• Monitor and resolve data-related issues in accordance with SLAs.

Skills

Data architecture understanding

ETL/ELT pipeline design

Experience with RDBMS

Familiarity with API integrations

Knowledge of cloud platforms

Understanding of data governance

Awareness of AI/ML fundamentals

Experience in BI frameworks

Strong documentation skills

Problem-solving ability

Education

Bachelor’s Degree in Computer Science or related field

Diploma in a relevant discipline with significant experience

Tools

Azure Data Factory

Databricks

Power BI

Job description

Industry: Financial Services/Takaful (Islamic Insurance)

The Data & AI Ops Engineer provides end-to-end technical expertise in designing, building, and operating scalable data and AI-enabled platforms. This role ensures reliable, high-quality data pipelines, models, and integrations that support analytics, reporting, and AI-driven solutions across the organization.

The position plays a key role in data architecture, API-based integration, performance optimization, and operational support, ensuring data availability across dashboards, applications, and backend systems while meeting SLA requirements for Incident Requests (IR) and Service Requests (SR).

Job Description

• Design, develop, and maintain scalable data pipelines and integration workflows for analytics, reporting, and operational systems
• Ensure data quality, consistency, and reliability through validation, cleansing, and transformation processes
• Collaborate with developers, business analysts, and system owners to define data requirements and deliver business-aligned solutions
• Optimize data storage and retrieval performance across databases, data lakes, and cloud platforms for batch and real-time processing
• Build, deploy, and enhance data models, APIs, and ETL/ELT frameworks in line with architecture and governance standards
• Monitor and resolve data-related IRs and SRs in accordance with SLA and operational expectations
• Support data-centric IT initiatives, including planning, coordination, risk mitigation, and stakeholder engagement
• Maintain clear documentation for data flows, schemas, and integration logic to support audit, compliance, and knowledge sharing
• Ensure seamless integration with core business systems and external platforms
• Continuously evaluate and adopt emerging data and AI technologies to improve efficiency and solution performance

Key Skills & Requirements

• Strong understanding of data architecture, data modeling, and ETL/ELT pipeline design
• Experience with enterprise data platforms (RDBMS required; Azure Data Factory, Databricks, Power BI are a plus)
• Familiarity with API-based integration (REST, SOAP, MQ)
• Knowledge of cloud platforms (Azure, AWS, or GCP)
• Understanding of data governance, security, privacy, lineage, and compliance requirements
• Awareness of AI/ML fundamentals, including chatbot solutions, predictive analytics, and model deployment
• Experience in BI and reporting frameworks for visualization and decision support
• Strong documentation, communication, and stakeholder engagement skills
• Ability to operate in dynamic environments, troubleshoot issues, and maintain operational stability

Education & Experience

• Bachelor’s Degree in Computer Science, Information Systems, Data Analytics, or related field
• Minimum 6 years hands-on experience in data engineering or data integration
• At least 3 years designing and supporting enterprise-scale data pipelines
• OR
• Diploma in a relevant discipline
• Minimum 8 years practical experience in data engineering or related domains
• At least 4 years in a senior or technical lead role supporting cross-functional data initiatives