
Senior Data Product Services Engineer
- Kuala Lumpur
- Permanent
- Full-time
- Design and enhance enterprise architecture, security models, and platform services for core data systems.
- Implement end-to-end security controls across cloud data platforms, ensuring compliance with regulatory and industry standards.
- Optimize system performance, availability, and scalability to support operational excellence.
- Contribute to technology modernization efforts and integrations with enterprise IT systems.
- Develop automated tools for security monitoring, vulnerability assessments, and identity management.
- Leverage DevOps practices, CI/CD pipelines, and Infrastructure-as-Code (IaC) to streamline platform deployment and consistency.
- Support disaster recovery planning and high availability for enterprise platforms.
- Collaborate with engineering and operations teams to ensure alignment between technical solutions and business goals.
- Offer strategic guidance on platform investments, risk management, and continuous improvement initiatives.
- Partner with senior engineers to shape future-facing technical roadmaps and reduce operational complexity.
- Bachelor’s degree or equivalent experience in Technology, Engineering, or a related field.
- proven experience in large-scale enterprise technology, platform operations, or security engineering.
- Proficiency with CI/CD practices, DevOps workflows, and Infrastructure-as-Code tools (e.g., AWS CDK, Azure Bicep).
- Strong understanding of ITIL frameworks, Agile methodologies, and enterprise governance.
- Hands-on experience with big data technologies such as Apache Spark, Hadoop, Kafka, and Flink.
- Proficient in working with cloud platforms (AWS, Azure, GCP) and cloud-native data tools (BigQuery, Snowflake, Redshift, Databricks).
- Programming expertise in SQL, Python, or Scala, with experience in data platform engineering.
- Solid knowledge of distributed systems, data modeling, and data warehousing architecture.
- Experience working with Microsoft Azure, AWS, Databricks, and Palantir.
- In-depth knowledge of data ingestion pipelines, data governance, and security practices.
- Proven ability to support multi-cloud environments—balancing cost, performance, and resiliency.
- Familiarity with distributed query processing, performance tuning, and data indexing.
- Exposure to both batch and real-time data streaming architectures.
- Experience operating in complex, regulated enterprise environments with a focus on compliance and risk management.
- Familiarity with AI/ML-driven data workflows and automation in data engineering contexts.