Top Data Engineer Jobs in Malaysia

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.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

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.

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.

Supports the design, development, and optimization of data pipelines, ETL/ELT processes, data models, and data quality workflows using Databricks, Python, SQL, and cloud-based platforms. The intern collaborates with engineers, architects, analysts, and stakeholders; participates in testing, deployment, Agile ceremonies, and documentation; and explores AI-assisted development, prompt engineering, and Generative AI proof-of-concepts for data engineering.

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.

Build, automate, and operate enterprise-scale data platforms using Databricks, Snowflake, Microsoft Fabric, Kubernetes, and cloud technologies. Responsibilities include Infrastructure as Code, CI/CD, GitOps, platform provisioning, reliability engineering, observability, security, governance, incident remediation, performance optimization, and FinOps. The role supports multi-environment deployments and self-service capabilities for data engineering and analytics teams.

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.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

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.

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.

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.

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.

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.