Data Engineer jobs in Malaysia

BJAK is building the next-generation insurance and financial services platform - designed to be intuitive, intelligent and personalised. Presently we are the largest insurance platform in Southeast Asia, and expanding globally with a strong focus on technology and product superiority. We are looking for talented, ownership-minded individuals. In return, expect growth, trust, autonomy, rewards, and impact.

As a Data Engineer, you will build and operate the data infrastructure that powers BJAK’s analytics, product insights, and downstream systems. You will work closely with analytics, product, and engineering teams to ensure data is reliable, accessible, and production‑ready at scale.

• Design, build, and maintain scalable, fault‑tolerant data pipelines (batch and/or streaming) for core business and product data.
• Ingest data from diverse sources including APIs, databases, event streams, and third‑party services, ensuring high data quality and reliability.
• Design and manage data models and storage layers (data warehouses, data lakes) that support analytics and downstream use cases.
• Partner with analytics, product, and engineering teams to deliver clean, well‑documented datasets that enable self‑service analytics and experimentation.
• Implement monitoring, logging, and alerting to ensure pipeline reliability, performance, and cost efficiency.
• Enforce data governance best practices, including access control, privacy, documentation, and data lineage.

• 3+ years of experience as a Data Engineer, data-heavy Backend Engineer, or similar role.
• Strong programming skills in Python and/or TypeScript, with solid SQL proficiency.
• Good understanding of data modeling, ETL/ELT concepts, and analytics workflows.
• Hands‑on experience with data warehouses (e.g. BigQuery, Snowflake).
• Experience building and operating production data pipelines.

• Experience with cloud platforms (especially GCP).
• Exposure to streaming systems (Kafka, Pub/Sub).
• Familiarity with dbt and analytics engineering practices.
• Experience with data lake technologies.
• Exposure to ML data pipelines or feature stores.

• Build and own data systems that operate at real scale
• Work on high‑impact, business‑critical data use cases
• High‑ownership role with autonomy and trust
• Flat structure – execution and results matter more than titles
• Competitive compensation and strong growth opportunities