Top Data Engineer Jobs in Kuala Lumpur

The ETL & Data Engineer will focus on ETL development and programming tasks, involving database design, SQL development, and Python scripting. This role also requires experience in agile methodologies, troubleshooting, and collaboration with technical experts, along with knowledge in various technologies including Oracle, Postgres, and CI/CD tools.

The Data Engineer will enhance the BI platform by prioritizing and implementing feature requirements, improving SSIS packages and ETL workflows, providing accurate data to users, and assisting in visualizing data in PowerBI. Documentation of data models and reports is also part of the role.

The Sr Cloud Data Engineer will lead data integration initiatives, collaborate with various stakeholders to ensure data alignment, and develop data management standards. Responsibilities include creating and reviewing data deliverables and developing and managing data pipelines and warehouse technologies.

The ETL & Data Engineer will implement large-scale data warehouse and analytics platforms in the financial services industry, focusing on data profiling, modeling, governance, visualization, and specific analytics domains like finance and credit risk.

The Senior Data Engineer will implement Quantexa’s technology to contextualize data across various domains. Responsibilities include constructing end-to-end data pipelines, deploying data processing systems, and collaborating with teams to address complex data challenges and ensure best practices are followed.

As an Entry-Level to Mid-Level Application Developer, you will design, code, test, and debug software applications primarily using Cloudera Hadoop. You will collaborate with teams to integrate applications, conduct testing to optimize performance, and stay updated on software engineering trends.

The Staff Data Engineer will lead a team in managing ETL processes, data management, and data modeling into the data warehouse. Responsibilities include developing data extractors, troubleshooting data discrepancies, automating data onboarding, and architecting solutions that meet business needs while interacting closely with APIs and deploying data into Snowflake.