Top Data Engineer Jobs in Johor Bahru

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.

Operate a 24x7 data centre network operations centre, monitoring network systems and infrastructure, troubleshooting incidents, maintaining routers, switches, firewalls, and related equipment, and performing upgrades and configuration changes. The role manages incidents, security alerts, documentation, change processes, performance optimization, hardware inventory, cabling, remote hands, and data centre operations while collaborating with technology, security, facilities, and vendor teams.

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.

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.

Perform routine security site walks in data halls, storage rooms and offices to validate door/badge readers and cameras/sensors; identify and report security incidents, safety hazards, and hardware anomalies; coordinate vendors for installs and retrofits; support critical security events; maintain daily status reports, attend briefings, comply with PPE and NDAs, and assist with access control onboarding and presence in leased spaces.

Apply data science and statistical methods to substrate manufacturing to improve yield, reduce defects and downtime. Build end-to-end analytics pipelines, predictive/prescriptive models, Spotfire dashboards, and support AI-driven root cause analysis while collaborating with process, quality, test/FA, and metrology 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 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.