Top Data Engineer Jobs in Malaysia

Provide L2 operational support for database systems, including incident, request, change, and problem management. Install, upgrade, patch, monitor, secure, and troubleshoot databases while maintaining availability and service-level targets. Manage access and backups, improve monitoring and operational procedures, create database reports and dashboards, and participate in 24/7 shift coverage and major incident response. Use AI and agentic solutions to automate workflows and improve IT operations reliability.

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.

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.

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.

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.

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.

Build full-stack tools, backend services, APIs, dashboards, and data workflows for assessing and improving AI training data and evaluations. Partner with quality and research teams to inspect agent trajectories, validate graders, measure quality, investigate workflow issues, and turn internal prototypes into reliable production platform capabilities.

Support the design, development, and optimization of data pipelines, ETL/ELT processes, data models, and cloud-based data solutions using Databricks, Python, and SQL. Perform data quality validation, documentation, testing, deployment, and troubleshooting. Collaborate with engineers, architects, analysts, and stakeholders while exploring AI-assisted development tools, generative AI, and prompt engineering for data engineering use cases.

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.

Analyzes quality and EHS data from multiple sources, develops Power BI dashboards and reports, identifies trends and anomalies, applies statistical and predictive modeling techniques, integrates TQM systems with analytics platforms, conducts risk assessments, and presents actionable recommendations to stakeholders and executives.

Develop and optimize group-level offline and real-time data warehouses, build tagging systems and feature libraries, support personalized recommendations, design scalable data models and assets, and establish data cleaning, quality monitoring, and alerting systems. The role requires strong SQL optimization, data warehouse and BI expertise, programming skills in Python, Scala, or Java, and familiarity with big data ecosystems.

Provides operational support, troubleshooting, data quality assurance, pipeline optimization, automation, infrastructure redesign, governance, and compliance. Collaborates with product, engineering, and business teams to deliver reliable data solutions and maximize data value. Requires experience with Python or Spark, Airflow, Terraform, ETL, databases, distributed computing, cloud services, workflow tools, version control, and big data frameworks.

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.

Build and own reliable batch and streaming data pipelines supporting trading, analytics, compliance, fraud detection, and AI/ML systems. Implement observability, data quality, anomaly detection, governance, data contracts, lineage, and access controls. Optimize warehouse performance and costs, develop scalable data models, resolve root-cause data issues, collaborate with business stakeholders, review code, and mentor engineers.

Designs and operates secure, scalable Azure infrastructure and modern data platforms. Responsibilities include landing zones, networking, identity, security, compute, infrastructure as code, monitoring, reliability, disaster recovery, data ingestion, transformation, modeling, governance, and performance optimization. The role supports mission-critical production workloads and partners with analytics, BI, security, and AI/ML teams. Required experience includes Azure infrastructure and data services, SQL, batch or streaming pipelines, cloud security, IaC, and modern analytics architectures.

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.

Provide L2 database operations support across incident, request, change and problem management for 24/7 coverage. Install, upgrade, patch, monitor, troubleshoot and ensure availability, backups, access controls and security compliance. Meet SLAs and metrics, develop procedures, build operational reports and dashboards, and leverage AI/agentic solutions to automate workflows and improve IT operations efficiency.

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.

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.