Top Data Engineer Jobs in Kuala Lumpur

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.

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.

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.

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.

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.

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 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 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.

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.

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.