Top Data Engineer Jobs in Kuala Lumpur

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.

Support APAC Claims Operations by producing and automating reports, reconciling multi-system data, resolving quality issues, and developing scalable dashboards. Build SQL queries, Python automation workflows, data validation processes, and documentation. Deliver ad hoc analysis and leadership insights while piloting AI applications such as claims classification, anomaly detection, document summarization, and natural-language querying. Partner across markets to improve reporting efficiency, data quality, and operational decision-making.

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.

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.

Lead mechanical design for data centre projects in Sydney, overseeing performance, energy efficiency, project delivery, client engagement, and mentoring junior engineers while incorporating industry innovations.

Design, develop, and manage enterprise data platforms and pipelines (ETL/ELT) across the organization. Optimize Oracle SQL/PLSQL, implement Snowflake-based data warehousing, build BI data sources/models for Tableau/Power BI/OBIEE, support CI/CD deployments, schedule jobs (Control-M), and collaborate with global engineering teams on data architecture and operational support.

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.

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.

Design, build, administer, and support Qlik Replicate data pipelines for near-real-time and batch replication across heterogeneous systems. Troubleshoot replication and performance issues, write complex SQL/PLSQL, perform Unix scripting, collaborate on data migrations and cloud modernization, document architectures, automate tasks, and provide Level 2/3 production support and stakeholder engagement.