Head of Data Engineering

• Build and maintain scalable, secure and high -performance data platforms (on -premise and cloud).
  
• Implement robust data integration pipelines to ingest data from multiple sources (batch and real -time) to support credit scoring, analytics and digital services.
  
• Design processes to create data insights, data features and operationalize analytics models.
  
• Operationalize data pipelines for automated and semi -automated execution.
  
• Implement reporting of data universe, data quality across the company.
  
• Oversee and deliver internal and external data extraction services in line with business needs and client SLAs.
  

• Collaborate with Data Governance & Compliance together with Cybersecurity & Risk team to ensure adherence to regulatory requirements (e.g. BNM, PDPA) and internal policies.
  
• Suggest and implement technology upgrades to data platforms.
  
• Support key initiatives including migration of the company’s data services to the cloud.
  

• Evaluate and adopt modern data engineering tools, cloud platforms (AWS, Azure, GCP) and big data technologies (Spark, Kafka, Hadoop, Databricks) including workflow tools and dashboarding.
  
• Champion automation, CI/CD for data pipelines, and DataOps best practices.
  
• Support AI/ML initiatives by ensuring availability of clean, structured, and accessible data.
  

• Work closely with data analytics, product and technology teams to deliver high -impact data solutions.
  
• Act as subject matter expert for data engineering, advising on opportunities and risks.
  

Requirements

• Bachelor’s degree or higher in Computer Science, Information Systems, Data Engineering or a related discipline.
  
• 10+ years of hands -on experience in data engineering with at least 3 years in a leadership role.
  
• Proven experience in designing and managing large -scale data platforms in financial services, fintech or technology sectors (credit reporting, or regulatory data environment is a plus).
  
• Understanding of financial industry and related data (CCRIS, SSM, Angkasa, Idaman) is a plus.
  
• Strong knowledge of data pipelines and data development platforms such as Hadoop, Spark/PySpark, Jupyter, cloud native services.
  
• Experience but not limited to AWS data & analytics native services is a plus: AWS Glue, Athena, Lake Formation, Redshift, EMR, SageMaker.
  
• Proficiency with ETL, Integration tools (e.g. AWS Glue, Talend, Informatica, Microsoft SSIS, Apache NiFi or equivalent).
  
• Strong SQL skills and experience with relational and non -relational databases (e.g. Postgres, SQL Server, Oracle, MongoDB).
  
• Knowledge of data governance, security, and privacy regulations (e.g. GDPR, PDPA).
  
• Experience with version control systems (e.g. Git), CI/CD pipelines, and workflow orchestration tools (e.g. Apache Airflow).
  
• Excellent problem -solving, analytical thinking, and debugging skills.
  
• Strong stakeholder management skills.
  
• High attention to detail, integrity and a strong sense of responsibility.
  
• Able to manage multiple projects in fast -paced environment