Senior Data Engineer
Summary
Bank Islam in Kuala Lumpur seeks a Senior Data Engineer to lead design and operation of scalable, secure, cost-efficient data platforms and pipelines, owning engineering standards, DataOps and data reliability, and mentoring engineers. Core stack: SQL, Python, Spark, Airflow, dbt, Docker/Kubernetes, Kafka, and public cloud.
About the role
Provide senior data engineering leadership to build and operate scalable, reliable, secure and cost-efficient data platforms and products. The role is accountable for engineering quality, DataOps, data reliability, technical standards and team capability.
Key responsibilities
Design and deliver scalable, reliable and cost-efficient data pipelines, services, products and reusable engineering components.
Establish engineering standards for coding, testing, deployment, documentation, data modelling and technical design.
Apply DataOps practices, including source control, peer review, automated testing, CI/CD, infrastructure-as-code and controlled releases.
Ensure data reliability through orchestration, observability, data-quality controls, metadata, lineage and operational monitoring.
Design data workloads for security, resilience, recoverability, performance and efficient resource consumption.
Diagnose and resolve complex data, pipeline, platform and performance issues.
Lead technical reviews, mentor data engineers and strengthen engineering capability across the team.
Communicate technical decisions, risks and trade-offs clearly, and collaborate with architecture, security, risk, analytics and business stakeholders.
About you
Bachelor's Degree in Computer Science, Information Technology, Data Engineering or a related discipline, or equivalent professional experience.
Minimum 8 years of relevant experience in data engineering, data platform engineering or enterprise data integration, including 2 years as a technical or team lead.
Strong proficiency in SQL and Python, with experience building production-grade data pipelines and components.
Hands-on experience with distributed data processing using Spark, PySpark or comparable technologies.
Experience with workflow orchestration and transformation frameworks such as Airflow and dbt.
Working knowledge of Docker and Kubernetes for deploying and operating data workloads.
Hands-on experience with batch and streaming ingestion, including change data capture, and with API-based and event-driven architectures, including Kafka or comparable technologies.
Experience with relational and non-relational databases, object storage, data lakes, lakehouses and analytical data platforms.
Experience designing and operating data platforms on a public cloud, including cost monitoring.
Strong DataOps capability across Git, automated testing, CI/CD, infrastructure-as-code and release management.