Top Data Engineer Jobs in Malaysia

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.

Build and operate scalable financial market data infrastructure, including source evaluation, ingestion, cleansing, modeling, real-time and batch processing, quality governance, reconciliation, monitoring, and data services. Develop Flink-based pipelines supporting trading, research, and AI applications. Partner with product, trading, compliance, procurement, and engineering teams to define data contracts, source strategies, and consistent financial data semantics across markets.

Develops, maintains, and optimizes data pipelines while ensuring data integrity and quality. Collaborates with Product, Business, and Analytics stakeholders to resolve data issues and support transformation requirements. The role requires programming, ETL, SQL and NoSQL databases, workflow and stream-processing tools, cloud services, containerization, version control, distributed computing, and big data expertise.

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 and maintain data pipelines, datamarts, data warehouses, and cloud-based solutions on AWS. Develop and optimize Databricks, EMR, Spark, and SQL jobs; deploy production systems using DevOps practices; improve query and schema performance; resolve data quality issues; extract structured data from unstructured sources; and collaborate across teams to deliver scalable, reliable data products.

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, maintains, and optimizes data pipelines while ensuring data integrity and quality. Partners with Product, Business, and Analytics teams to resolve data issues and support transformation requirements. Uses programming languages, SQL and NoSQL databases, ETL, workflow management, stream processing, cloud platforms, containerization, version control, and distributed computing technologies.

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.

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

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.

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.

Oversee day-to-day data center facility operations, preventive and reactive maintenance, plant inspections, incident escalation, vendor coordination, work orders, compliance, and HSE activities. The role supports electrical and mechanical infrastructure, contractor permits, maintenance scheduling, budgeting, reporting, space planning in AutoCAD, and supervision of engineering technicians in a critical Tier III environment.

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 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.

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.

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.

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 and implement data architecture and secure integration patterns to move insurance, reinsurance, and investment feeds into and out of Oracle Fusion Financials (including AHCS). Lead data migration, testing (SIT/NFT), quarterly upgrades, data governance, and collaborate with cross-functional and cross-geo teams to ensure data integrity, security, and performance for IFRS 17 and consolidation processes.