We are currently looking for one (1) Data and AI Ops Engineer,
preferably local candidate
but expat is also welcomed. We will review the submissions and respond within
three weeks
if the candidates are shortlisted. If you do not hear from us within this period, the profiles are considered not shortlisted.
1st priority
- Malaysian profile,
2nd
Expatriate in Malaysia
Education/Work Experience
Bachelor's degree
in Computer Science, Computer Engineering, Information Systems, Data Analytics or a related field, with
at least 5 years of hands-on experience
in data engineering, data integration, or analytics platform development, including
a minimum of 4 years
in designing and maintaining enterprise-grade data pipelines and solutions; OR
Diploma
in a relevant discipline, with
a minimum of 5 years of practical experience
in data engineering or related domains, including
at least 4 years
in a technical lead or senior engineering capacity, supporting cross-functional data initiatives and system integration efforts
Knowledge:
Understanding of
data architecture principles
, including data modeling, pipeline design, ETL/ELT frameworks, and distributed data processing
Has knowledge in
enterprise data platforms
and tools, at least in RDBMS. Knowledge in Azure Data Factory, Databricks, Power BI, and other modern analytics ecosystems is a plus.
Familiarity with
API-based integration
and data exchange protocols (e.g., REST, SOAP, MQ), enabling seamless connectivity between systems and platforms
Knowledge of
cloud data services
and infrastructure (e.g., Azure, AWS, GCP).
Understanding of
data governance, privacy, and compliance standards
, including data lineage, access control, and audit readiness
Awareness of
AI and machine learning fundamentals
, particularly in the context of chatbot development, predictive analytics, and model deployment
Experience in
business intelligence and reporting frameworks
, enabling effective visualization, role-based access, and decision support
Up-to-date perspective on
emerging data technologies
, trends, and best practices relevant to enterprise analytics and AI-driven solutions
Skills:
Hands-on in design, build, and optimize scalable data pipelines to support analytics, reporting, and AI initiatives
Hands-on in managing end-to-end data engineering workflows, including ingestion, transformation, validation, and orchestration across cloud and on-prem platforms
Effective in supporting data operations and enhancement activities, ensuring data quality, availability, and performance for business-critical applications
Good communication and interpersonal skills, with the ability to translate complex data concepts for technical and non-technical audiences
Excellent in documenting data processes, producing technical specifications, and preparing stakeholder-facing reports in English
Capable of collaborating within cross-functional teams, including developers, analysts, and business users, to deliver integrated data solutions
Comfortable navigating dynamic environments, resolving data-related issues, and maintaining operational resilience under pressure