全职 Manager, Data Operations - Analytics 工作, 薪水, International Medical University Federal Territory 公司招聘中 - Ricebowl

The Manager, Data Operations & Analytics will lead the University’s data operations and analytics function, working with stakeholders across academic, corporate and ITS functions to deliver reliable, scalable and data-driven solutions aligned with institutional goals.

Key Responsibilities

• Lead the daily operations of the enterprise data lake, data warehouse and data marts, ensuring reliable data ingestion, processing and monitoring.
• Set the architecture, standards and best practices for scalable ETL/ELT pipelines integrating data from multiple sources.
• Engage stakeholders to gather and prioritise business requirements and translate them into effective technical solutions.
• Establish and enforce standards for data quality, validation, cleansing and transformation.
• Guide the development and implementation of dashboards, reports and advanced analytics solutions that support strategic and operational decision-making.
• Oversee the development, validation and performance monitoring of predictive and prescriptive models.
• Ensure data reliability, security and compliance with internal governance requirements in collaboration with ITS Cybersecurity.
• Lead cross-functional data and analytics projects, including planning, budgeting, vendor coordination and reporting to senior stakeholders.
• Promote the use of machine learning, segmentation, optimisation and visualisation to generate actionable insights.
• Set technical standards for coding, version control, CI/CD and reproducible development practices.
• Lead, mentor and develop the data team, ensuring effective delivery and continuous capability development.
• Serve as the analytics subject matter expert, advising stakeholders and leadership on the design and interpretation of reports, dashboards and analytical outputs.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Information Technology or a related field. A master’s degree is an advantage.
• Minimum 10 years of overall work experience, including at least five years in a lead or managerial capacity within data engineering, analytics or data operations.
• Proven experience managing enterprise data platforms, data pipelines and large-scale analytics projects.
• Experience in project planning, budgeting, vendor management and reporting to senior stakeholders.
• Experience contributing to data governance policies or organisational data strategy.
• Strong leadership and mentoring skills, with experience developing high-performing teams.
• Excellent stakeholder management and communication skills, with the ability to translate technical concepts into business value.
• Strategic, organised and results-oriented, with strong analytical and problem-solving capabilities.
• Proactive, adaptable and committed to continuous learning and promoting a data-driven culture.

Technical Competencies

• Strong knowledge of data architecture, ETL/ELT pipelines and enterprise data platforms, including data lakes, data warehouses and data marts.
• Proficiency in SQL, Python and cloud platforms such as AWS, Azure or GCP.
• Experience with cloud-native data services such as AWS Glue, Athena, S3 and Redshift.
• Familiarity with distributed computing technologies such as Apache Spark or Hadoop.
• Experience in machine learning, advanced analytics, statistical modelling or artificial intelligence.
• Ability to guide and evaluate predictive and prescriptive models, including model validation and performance monitoring.
• Knowledge of data governance, metadata management, data quality, data security and privacy requirements.
• Familiarity with version control, CI/CD practices and collaborative development tools such as Git and JIRA.
• Ability to assess vendor solutions and integrate third-party platforms into enterprise data workflows.

For more information, please click the job link as below:-

*************

Please be advised that only shortlisted applicants will be notified either through email or by phone. Thank you.