Data Operations & Analytics Manager

IMU University; Kuala Lumpur; On-site; MYR 180,000 - 260,000; Full time

Job summary

IMU University in Kuala Lumpur seeks a Manager to lead the data operations and analytics function, delivering data-driven solutions aligned with institutional goals. You will oversee the data platform, ETL/ELT pipelines, and governance to enable informed decision‑making.

The role requires strong leadership, stakeholder engagement, and hands-on guidance of dashboards, models, and data products, with emphasis on quality, security, and scalable architecture.

Qualifications

• 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.

Responsibilities

• Oversee Operations of the Data Platform - Provide leadership and oversight for the daily operations of the enterprise data lake and data warehouse. Ensure efficient data ingestion, processing, and monitoring practices are in place while delegating operational tasks to the team.
• Guide Design and Development of ETL/ELT Pipelines - Lead the architectural direction and standards for ETL/ELT processes integrating data from multiple sources into the Data Lake, Data Marts, and Data Warehouse. Review team deliverables to ensure scalability, efficiency, and adherence to best practices.
• Engage Stakeholders and Align Business Requirements - Collaborate with business units to gather and prioritize requirements. Translate these into technical specifications and oversee the delivery of data solutions that enable decision‑making and improve operational efficiency.
• Ensure Data Quality and Integrity - Define and enforce data quality standards. Supervise data cleansing, validation, and transformation efforts to maintain accurate, consistent, and reliable datasets across systems.
• Deliver Data Science and Analytics Solutions - Guide the development and implementation of dashboards, reports, and advanced analytics to meet strategic needs. Ensure data products are actionable, well‑adopted, and aligned with the organization’s objectives.
• Drive Data Reliability, Security, and Compliance - Oversee methods and frameworks to enhance data reliability and ensure compliance with internal data governance policies in close collaboration with ITS Cybersecurity.
• Supervise Model and Algorithm Development - Provide technical direction on the development of predictive and prescriptive models. Ensure model validation, performance monitoring, and ethical use of AI/ML techniques.
• Lead Cross-functional Collaboration - Coordinate with multiple functional teams—including academic, corporate, and ITS - to implement data solutions, manage project delivery, and communicate analytical insights.
• Monitor and Improve Model Performance - Establish frameworks for monitoring and evaluating machine learning model performance and data accuracy. Guide the implementation of tools and processes for continuous improvement.
• Drive Innovation in Advanced Analytics - Promote the use of machine learning, segmentation, and visualization to uncover insights from data. Encourage experimentation and proof‑of‑concept development by the team or partners.
• Promote Best Practices in Coding and Tooling - Set technical direction for the use of Python, JS, SQL, or related technologies. Ensure adherence to standards in coding, versioning, and reproducibility across the team.
• Act as Analytics Subject Matter Expert (SME) - Provide expert guidance to stakeholders and leadership teams on the design and interpretation of advanced reports, dashboards, and analytics outputs.

Skills

Leadership

Stakeholder management

Communication skills

Problem-solving

Strategic planning

Education

Bachelor's degree in Computer Science, Data Science, IT or related

Master's degree (advantage)

Tools

SQL

Python

AWS

Azure

GCP

AWS Glue

Athena

S3

Redshift

Spark

Job description

The Manager will lead the data operations and analytics function, working with stakeholders across the university to deliver data-driven solutions aligned with institutional goals. Key responsibilities include:

• Oversee Operations of the Data Platform - Provide leadership and oversight for the daily operations of the enterprise data lake and data warehouse. Ensure efficient data ingestion, processing, and monitoring practices are in place while delegating operational tasks to the team.
• Guide Design and Development of ETL/ELT Pipelines - Lead the architectural direction and standards for ETL/ELT processes integrating data from multiple sources into the Data Lake, Data Marts, and Data Warehouse. Review team deliverables to ensure scalability, efficiency, and adherence to best practices.
• Engage Stakeholders and Align Business Requirements - Collaborate with business units to gather and prioritize requirements. Translate these into technical specifications and oversee the delivery of data solutions that enable decision‑making and improve operational efficiency.
• Ensure Data Quality and Integrity - Define and enforce data quality standards. Supervise data cleansing, validation, and transformation efforts to maintain accurate, consistent, and reliable datasets across systems.
• Deliver Data Science and Analytics Solutions - Guide the development and implementation of dashboards, reports, and advanced analytics to meet strategic needs. Ensure data products are actionable, well‑adopted, and aligned with the organization’s objectives.
• Drive Data Reliability, Security, and Compliance - Oversee methods and frameworks to enhance data reliability and ensure compliance with internal data governance policies in close collaboration with ITS Cybersecurity.
• Supervise Model and Algorithm Development - Provide technical direction on the development of predictive and prescriptive models. Ensure model validation, performance monitoring, and ethical use of AI/ML techniques.
• Lead Cross-functional Collaboration - Coordinate with multiple functional teams—including academic, corporate, and ITS - to implement data solutions, manage project delivery, and communicate analytical insights.
• Monitor and Improve Model Performance - Establish frameworks for monitoring and evaluating machine learning model performance and data accuracy. Guide the implementation of tools and processes for continuous improvement.
• Drive Innovation in Advanced Analytics - Promote the use of machine learning, segmentation, and visualization to uncover insights from data. Encourage experimentation and proof‑of‑concept development by the team or partners.
• Promote Best Practices in Coding and Tooling - Set technical direction for the use of Python, JS, SQL, or related technologies. Ensure adherence to standards in coding, versioning, and reproducibility across the team.
• Act as Analytics Subject Matter Expert (SME) - Provide expert guidance to stakeholders and leadership teams on the design and interpretation of advanced reports, dashboards, and analytics outputs.

Job Responsibilities

The Manager will lead the data operations and analytics function, working with stakeholders across the university to deliver data-driven solutions aligned with institutional goals. Key responsibilities include:

• Oversee Operations of the Data Platform - Provide leadership and oversight for the daily operations of the enterprise data lake and data warehouse. Ensure efficient data ingestion, processing, and monitoring practices are in place while delegating operational tasks to the team.
• Guide Design and Development of ETL/ELT Pipelines - Lead the architectural direction and standards for ETL/ELT processes integrating data from multiple sources into the Data Lake, Data Marts, and Data Warehouse. Review team deliverables to ensure scalability, efficiency, and adherence to best practices.
• Engage Stakeholders and Align Business Requirements - Collaborate with business units to gather and prioritize requirements. Translate these into technical specifications and oversee the delivery of data solutions that enable decision‑making and improve operational efficiency.
• Ensure Data Quality and Integrity - Define and enforce data quality standards. Supervise data cleansing, validation, and transformation efforts to maintain accurate, consistent, and reliable datasets across systems.
• Deliver Data Science and Analytics Solutions - Guide the development and implementation of dashboards, reports, and advanced analytics to meet strategic needs. Ensure data products are actionable, well‑adopted, and aligned with the organization’s objectives.
• Drive Data Reliability, Security, and Compliance - Oversee methods and frameworks to enhance data reliability and ensure compliance with internal data governance policies in close collaboration with ITS Cybersecurity.
• Supervise Model and Algorithm Development - Provide technical direction on the development of predictive and prescriptive models. Ensure model validation, performance monitoring, and ethical use of AI/ML techniques.
• Lead Cross-functional Collaboration - Coordinate with multiple functional teams—including academic, corporate, and ITS - to implement data solutions, manage project delivery, and communicate analytical insights.
• Monitor and Improve Model Performance - Establish frameworks for monitoring and evaluating machine learning model performance and data accuracy. Guide the implementation of tools and processes for continuous improvement.
• Drive Innovation in Advanced Analytics - Promote the use of machine learning, segmentation, and visualization to uncover insights from data. Encourage experimentation and proof‑of‑concept development by the team or partners.
• Promote Best Practices in Coding and Tooling - Set technical direction for the use of Python, JS, SQL, or related technologies. Ensure adherence to standards in coding, versioning, and reproducibility across the team.
• Act as Analytics Subject Matter Expert (SME) - Provide expert guidance to stakeholders and leadership teams on the design and interpretation of advanced reports, dashboards, and analytics outputs.

Job Requirement

• 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.