- Kuala Lumpur, Kuala Lumpur Kuala Lumpur WP Kuala Lumpur Malaysia
Working Location
Job Description
Responsibilities
Key Responsibilities
Platform Design & Engineering
· Design and implement scalable, resilient, and secure data platforms (data lakes, lakehouse, data warehouses).
· Build and maintain distributed data systems that support batch, streaming, and real-time processing.
· Develop reusable frameworks and platform services to standardize data ingestion, processing, and access.
Data Pipeline Development
· Build and maintain robust ETL/ELT pipelines using modern orchestration tools.
· Ensure data quality, lineage, observability, and governance are embedded within pipelines.
· Optimize data workflows for performance, cost efficiency, and reliability.
Cloud & Infrastructure Management
· Deploy, manage, and optimize data platforms on cloud providers (Azure, AWS, GCP).
· Implement Infrastructure as Code (IaC) using tools like Terraform, ARM/Bicep, or CloudFormation.
· Monitor and manage platform performance, availability, and scalability.
Data Governance & Security
· Implement data governance frameworks, including cataloging, classification, and lineage tracking.
· Ensure compliance with data security and privacy standards (e.g., GDPR, PDPA).
· Manage access control, encryption, and auditing mechanisms.
DevOps & Automation
· Build CI/CD pipelines for data platform components.
· Automate deployments, monitoring, and alerting.
· Apply SRE principles to improve platform reliability and availability.
Collaboration & Enablement
· Partner with data engineers, data scientists, and business stakeholders to deliver data solutions.
· Provide platform best practices and guidelines to engineering teams.
· Support self-service data capabilities for analytics and AI use cases.
Requirement
Education & Experience
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 5+ years of experience in data engineering, platform engineering, or related roles.
• Experience with AI/ML data pipelines and feature stores.
• Knowledge of data security frameworks and zero-trust architecture.
• Certification in cloud platforms (e.g., Azure Data Engineer, AWS Certified Data Analytics).
• Familiarity with FinOps practices for data platform cost optimization.
Technical Skills
• Strong programming skills (Python, Java, Scala, or similar).
• Experience with distributed data processing frameworks (Spark, Flink, or equivalent).
• Proficiency in SQL and data modeling techniques.
Cloud & Data Technologies
• Hands-on experience with cloud data services:
o Azure: Data Factory, Synapse, Databricks, ADLS, HDInsight
o AWS: S3, Glue, Redshift, EMR
o GCP: BigQuery, Dataflow, Composer, Managed Spark, Hadoop
o Specialized Big Data: Snowflake, Databricks, Cloudera, Oracle Big Data Services
• Experience with modern data architectures (Lakehouse, Data Mesh, Data Fabric).
Data Pipeline & Orchestration
• Tools such as Airflow, Azure Data Factory, Prefect, or Dagster, Docket, Git.
• Experience with streaming platforms (Kafka, Redpanda Event Hubs, Kinesis).
DevOps & Infrastructure
• Familiarity with containerization (Docker) and orchestration (Kubernetes).
• Experience with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins).
• Infrastructure as Code (Terraform preferred).
Data Governance, Security & Observability
• Experience with tools like Collibra, Purview, DataHub, Prometheus, Grafana, OpenLineage/ Apache Ranger
• Understanding data quality frameworks and monitoring tools.
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