
Data Platform Services Engineer
- Kuala Lumpur
- Permanent
- Full-time
- Contribute to the design and implementation of enterprise-wide technology architecture, platform engineering, and security frameworks.
- Lead and support end-to-end security initiatives across the data platform, ensuring alignment with compliance, regulatory, and industry standards.
- Drive operational excellence by enhancing system performance, reliability, and scalability.
- Support modernization initiatives by integrating cloud-native data platforms with enterprise IT systems.
- Assist in developing and deploying automated monitoring, vulnerability detection, and identity access management (IAM) solutions.
- Apply DevOps, CI/CD, and Infrastructure-as-Code (IaC) practices to streamline deployments and improve infrastructure consistency.
- Collaborate on disaster recovery strategies and build high-availability platform solutions.
- Work with engineering and operations teams to align platform capabilities with evolving business needs.
- Provide insights on platform investments, operational risks, and areas for continuous improvement.
- Contribute to long-term technical roadmaps in partnership with senior engineers, focused on scalability, automation, and risk reduction.
- Bachelor's degree in Computer Science, Engineering, or a related technical field.
- 2–4 years of experience in enterprise technology, platform operations, or cybersecurity in large-scale environments.
- Hands-on experience with CI/CD, DevOps methodologies, and IaC tools (e.g., AWS CDK, Azure Bicep).
- Solid understanding of ITIL, Agile delivery models, and enterprise governance practices.
- Proficiency in big data technologies: Apache Spark, Kafka, Flink, Hadoop, etc.
- Strong experience with cloud platforms such as AWS, Azure, or GCP, and data services like Snowflake, BigQuery, Redshift, and Databricks.
- Proficient in SQL, Python, or Scala, with hands-on data platform engineering experience.
- Familiarity with data warehousing, data modeling, and distributed system architecture.
- Technical expertise in Azure, AWS, Databricks, and Palantir.
- Experience with designing and supporting large-scale data ingestion, governance, and visualization pipelines.
- Proven ability to manage multi-cloud data environments with an emphasis on cost efficiency, performance, and resilience.
- Understanding of performance tuning, data indexing, and distributed query optimization.
- Exposure to real-time and batch streaming data architectures.
- Proven success working in global, highly regulated environments—managing compliance, data security, and enterprise-level risk.
- Familiarity with applying AI/ML-driven automation in data engineering to optimize workflows and enhance platform efficiency.