Top Data Engineer Jobs in Malaysia

The Sr IT Engineer provides L2 support for database operations, ensuring system availability, managing incidents, and improving monitoring and reporting processes.

Design, build, and maintain scalable ETL/ELT data pipelines and data lake/warehouse solutions using Snowflake or Databricks on Azure. Implement ingestion, transformation, data quality checks, monitoring, and performance optimization. Collaborate with Analytics, Data Science, Product, and Engineering on architecture and data consumption; contribute to governance and CI/CD practices.

The Senior Data Engineer will develop and maintain a Data Management Platform, ensuring its stability and compliance while leading a project team in delivering data solutions and optimizing processes.

Assist in transforming data for analysis by developing structured data sets and models. Support ETL process and data migrations.

Design, maintain, and optimize Power BI reports and data pipelines. Build SQL views/queries, improve Python ETL scripts integrating SAP, Salesforce, ServiceMax, SFTP and MES, troubleshoot refresh/gateway issues, document logic, and collaborate with stakeholders to deliver analytics solutions.

Design, build, and manage Microsoft Fabric data architectures and ETL pipelines. Transform raw data for analytics and AI, optimize models/pipelines for performance and cost, ensure data quality and governance, and support AI teams for RAG/LLM workloads. Document processes and collaborate with stakeholders.

Design, build, and automate ETL/data pipelines to support AI/ML workflows; integrate backend systems and APIs; ensure data governance, security, and compliance; deploy ML models to production; use SQL, Python, Spark, Hadoop, Kafka and cloud data services.

Design, operate, and optimize data pipelines and infrastructure to ensure data quality, enable analytics, and support product and engineering teams. Provide operational support, troubleshoot production issues, automate processes, improve governance, and collaborate cross-functionally to maximize data-driven business impact.

Design, build and maintain ETL pipelines and data workflows, ensure data quality and governance, support cross-functional teams, integrate big-data tools, and automate processes to enable analytics and growth.

Design, build, and maintain data pipelines and Azure data warehouse infrastructure. Ensure data quality, governance, performance tuning, automation, and provide BI insights using Power BI or QlikSense.

Design, build, and maintain scalable ETL and real-time data processing systems on Databricks and AWS. Develop Spark-based pipelines, ensure data quality and governance, implement security controls, automate infrastructure with Terraform, and collaborate with analysts and engineers to resolve issues and optimize performance.

Design, build, and productionize data science solutions and scalable ETL/ELT pipelines. Create self-service BI (Tableau/Power BI/Superset), ensure data integrity across BI platforms, deploy ML models using R/Python, manage CI/CD and workflow tools, and work within Agile/Scrum to prioritize and deliver data products.

Design, build, and maintain centralized data systems, pipelines, warehouses, and lakes. Implement ETL processes, optimize data flow, produce analytics and reports, support stakeholders, ensure cross-region data security, and create tooling for product and business insights.

The Data Engineer will manage database design, ETL development, T-SQL programming, and optimize performance across SQL Server environments while supporting data integrity and migrations.

Provide L2 support for database operations, ensuring high availability, compliance and quality service. Manage incidents, monitor performance, and build reports.

Manage the data lifecycle, drive the data platform roadmap, establish best practices in analytics, reporting, and data governance while overseeing a team of engineers and analysts.

Design, build, and optimize data pipelines, warehouses, datamarts, and dimensional models on AWS. Develop schemas and SQL, tune queries and performance, resolve data quality issues, and provide operational support including CI/CD, monitoring, incident response, and database administration to enable data-driven decision making.

Apply data science and statistical methods to substrate manufacturing to improve yield, reduce defects and downtime. Build end-to-end analytics pipelines, predictive/prescriptive models, Spotfire dashboards, and support AI-driven root cause analysis while collaborating with process, quality, test/FA, and metrology teams.

Design and execute tests for end-to-end data flows across MongoDB and Azure services, automate UI/API/data-layer tests, validate Power BI reports and SQL/MongoDB transformations, integrate tests into CI/CD, and collaborate with engineering and product teams to identify, document, and resolve data defects.

Own and operate the cloud data platform, maintain IaC, optimize warehouse cost/performance, build and maintain batch and real-time ingestion and transformation pipelines, operate BI platform, and design AI-powered applications and agents (RAG/LLM-backed). Partner with stakeholders, mentor teammates, document runbooks, and drive data platform reliability and security.

Act as a bridge between business stakeholders and IT teams by analyzing and translating business needs into technical requirements, managing backlogs, facilitating UAT, and recommending system enhancements.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Develop and deploy fault detection, predictive analytics, APC and ML models for manufacturing. Perform EDA, validate data quality, monitor model performance, collaborate with cross-functional teams, and lead continuous improvement to improve yield, cycle time, and equipment performance.

The Staff Data Platform Engineer will design, build, and maintain scalable backend services and data pipelines, promoting cloud-native solutions and collaborating across teams to enhance analytics and operational intelligence.

The Senior Site Reliability Engineer will enhance data platform reliability on AWS, automating processes and collaborating on incident response and architecture improvements for data services.