Top Data Engineer Jobs in Malaysia

Provides L2 database operations support across incidents, requests, changes, and problems in a 24/7 environment. Installs, upgrades, patches, monitors, and troubleshoots database systems while managing access, backups, security compliance, performance, and availability. Develops operational procedures, reports, dashboards, and monitoring improvements, and applies AI-driven automation to improve IT operations and service reliability.

Design and develop GIS solutions, web applications, dashboards, and reporting tools for infrastructure projects. Migrate, cleanse, assess, and harmonize complex geospatial datasets across GIS platforms, including QGIS and Esri environments. Integrate GIS with enterprise systems using FME, SQL, and Python, while automating recurring processes and improving data quality, scalability, and operational efficiency. The role also supports GIS transformation projects and requires fluent German and strong English communication skills.

Develop and maintain reliable, scalable data pipelines and data products for analytics and operational reporting. Monitor performance, troubleshoot failures, investigate data issues, and implement automated validation, reconciliation, and quality monitoring. Collaborate with software engineers, analytics teams, and data consumers to deliver trusted data products and improve data platform reliability using AWS services.

Build and operate scalable Databricks data pipelines, ingest API-based data, develop dimensional and medallion data models, and maintain testing, orchestration, CI/CD, monitoring, and data quality practices. Collaborate with technical and business stakeholders, use AI-assisted development tools, improve automation, and contribute to engineering standards, documentation, and knowledge sharing.

Design, build, and operate the organization’s data platform across ingestion, transformation, orchestration, warehousing, and cloud infrastructure. Develop reliable batch and streaming pipelines, data models, contracts, quality checks, governance, privacy controls, and observability. Partner with analytics, research, product, and engineering teams to translate requirements into trustworthy data systems. This is a six-month contractor assignment requiring advanced SQL and Python, modern warehouse, orchestration, infrastructure, and data modeling expertise.

Design, build, and operate the organization’s data platform, including data models, batch and streaming pipelines, orchestration, warehouses, cloud infrastructure, and governance controls. Ensure reliable, observable, high-quality data through contracts, monitoring, privacy protections, and anonymization. Collaborate with analytics, research, product, and engineering teams to translate requirements into scalable data solutions.

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.

Build and maintain scalable data models, marts, semantic layers, and knowledge layers that support analytics and AI agents. Establish data quality, governance, lineage, metrics standards, and observability frameworks. Enable self-service analytics, optimize pipelines and queries, and collaborate with analysts, engineers, product teams, and business stakeholders to deliver reliable data and agentic analytics solutions.

Design and build Renesas’s governed enterprise data platform using Azure Databricks, Unity Catalog, Delta Lake, and Spark. Develop scalable ELT/ETL pipelines, data models, catalogs, ingestion strategies, and medallion architectures. Implement governance, PII classification and masking, data quality, security, CI/CD, and DevOps practices. Collaborate with functional teams, support SAP and manufacturing data domains, contribute to architecture decisions, and mentor junior engineers.

Plan, coordinate, supervise, and deliver mechanical and electrical works for high-rise or data center projects. Responsibilities include contractor and consultant coordination, drawing and material review, progress and quality monitoring, technical issue resolution, subcontractor management, testing and commissioning, authority compliance, safety, handover, and close-out documentation. Senior and manager roles additionally lead teams, control budgets, evaluate variations, and report project progress.

Combine supplier quality engineering with data analytics to build ELT pipelines, validate data, create automated Spotfire/Power BI dashboards, perform statistical modeling to monitor and predict manufacturing variances, and drive continuous improvement with supplier and internal teams. Provides rotational weekend support.

Lead supplier quality analytics for outsourced foundry, assembly, and test subcontractors. Build ELT pipelines, data validation, automated testing, and interactive dashboards (Spotfire/PowerBI). Use statistical modeling to track, predict, and resolve manufacturing variability, support continuous improvement, and provide weekend rotational support.

Lead supplier quality analytics for outsourced foundry, assembly and test partners. Build and maintain ELT pipelines, implement data validation and governance, create real-time dashboards (Spotfire/PowerBI), perform statistical analysis to detect drifts and defects, and drive continuous improvement and corrective actions. Support weekend rotation.

Designs, develops, and maintains scalable sales data pipelines, ETL/ELT processes, data models, and warehouse solutions using Snowflake, SQL Server, DBT, SQL, and GitLab. Troubleshoots complex data issues, improves data quality and performance, supports migration and modernization initiatives, and drives automation. Partners with Sales Operations, IT, BI, and analytics teams to deliver reliable, timely, and actionable sales data.

Design, develop, and maintain scalable AWS data platforms, pipelines, data lakes, and warehouses for enterprise analytics, reporting, and regulatory needs. Build ETL/ELT and batch or near-real-time processing using Python, SQL, PySpark, Apache Iceberg, and Terraform. Support data governance, quality, security, disaster recovery, monitoring, CI/CD, and business continuity. Collaborate with stakeholders, document solutions, conduct knowledge transfer, mentor junior engineers, and apply AI-assisted development practices.

Design and document conceptual, logical, physical, and dimensional data models supporting marketing segmentation, personalization, analytics, and activation. Analyze source systems, define mappings and data structures, partner on ETL/ELT pipelines, validate data with advanced SQL, optimize performance, and establish data quality, governance, metadata, and lineage standards. Support data users, incident response, monitoring, CI/CD, and database administration while collaborating with marketing, engineering, analytics, and platform stakeholders across APAC.

Build and maintain big data delivery systems, analytics platforms, monitoring dashboards, and data reports for compliance, trading, and business teams. Govern data assets through warehouse documentation and field-level standards. Develop data agents and automated retrieval workflows, while sourcing data through web scraping and API ingestion. Use Python, SQL, big data frameworks, OLAP engines, Java, cloud-native deployment tools, and AI coding technologies.

Supports and manages cloud-based big data platforms, ETL tools, databases, repositories, and data pipelines. Responsibilities include monitoring, troubleshooting production issues, developing diagnostic tools, optimizing analytics systems, managing support agents, maintaining SLAs, improving processes, and preventing recurring incidents. The role requires Azure big data expertise, programming experience in Python, Java, or Scala, advanced SQL, large-scale data processing knowledge, Power BI and Alteryx experience, strong communication, and ITIL knowledge.

Supports the design and development of scalable end-to-end data pipelines and cloud data platforms. Responsibilities include improving data reliability and quality, implementing data management practices, supporting automation and process improvements, ensuring secure and cost-effective solutions, and recommending system enhancements. The intern collaborates with global teams and gains hands-on experience in data engineering, project coordination, and innovation management.

Develops technically compliant and commercially competitive proposals for data center and industrial power generation projects. Responsibilities include tender analysis, system and equipment selection, cost estimation, supplier and OEM coordination, technical documentation, design verification, inspection and testing, commissioning support, and ISO 9001 quality compliance. The role requires collaboration with engineering, project, supplier, and client teams across Southeast Asia, with periodic travel for inspections and technical presentations.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Senior engineering role to design, implement, optimise and support cloud, data, and AI security solutions. Provide SME guidance, build configuration baselines and automation, support BAU operations and incident response, and collaborate with architects and product teams to deliver secure, scalable systems.

Lead scalable data engineering solutions using Cognite Data Fusion, including integrations, data extraction, modeling, cleansing, pipelines, and analysis. Collaborate with data scientists, architects, project managers, customers, and partners to deliver industrial data solutions. Conduct code reviews, apply DevOps and engineering best practices, contribute to Cognite tools and SDKs, and help prioritize product offerings based on customer needs.

Design, implement, and maintain scalable batch and streaming data pipelines on Databricks using Spark and Delta Lake. Collaborate with engineers, data scientists, architects, and clients to deliver production-ready solutions supporting analytics, machine learning, and business intelligence. Optimize performance and processing costs while supporting data quality, governance, security, CI/CD, orchestration, and version control best practices. Share knowledge and mentor junior engineers in an international consulting environment.

Designs and maintains enterprise data platforms, scalable pipelines, data models, cloud analytics environments, and AI-enabled solutions. Develops generative AI and RAG use cases, vector search, AI agents, workflow orchestration, integrations, and enterprise decision-support capabilities. Requires strong Python and SQL skills, experience with production data engineering, cloud platforms, semantic data models, AI governance, and tools including Microsoft Fabric, Azure AI Foundry, Copilot, OneLake, AWS Redshift, and Amazon Bedrock. This is an onsite role in Johor Bahru or Penang.