Data Operations & Analytics Manager

IMU University; Kuala Lumpur; On-site; MYR 300,000 - 420,000; Full time

Job summary

IMU University seeks a senior Manager to lead the data operations and analytics function, partnering with university stakeholders to deliver data-driven solutions that support institutional goals.

You will oversee daily operations of the data platform, guide ETL/ELT development, ensure data quality, and drive advanced analytics with dashboards and models while ensuring security and governance.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Information Technology or related field.
• Minimum 10 years of overall work experience with at least 5 years in lead/managerial capacity within data engineering/analytics/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 organizational data strategy.
• Strong leadership and mentoring skills with experience developing high-performing teams.
• Excellent stakeholder management and the ability to translate technical concepts into business value.
• Strategic, organized and results-oriented with strong analytical/problem-solving capabilities.
• Proactive, adaptable and committed to continuous learning and data-driven culture.
• Strong knowledge of data architecture, ETL/ELT pipelines and enterprise data platforms (lakes, warehouses, marts).
• Proficiency in SQL, Python and cloud platforms (AWS, Azure or GCP).
• Experience with cloud-native data services such as AWS Glue, Athena, S3 and Redshift.
• Familiarity with distributed computing (Apache Spark/Hadoop).
• Experience in machine learning, advanced analytics, statistical modelling or AI.
• Ability to guide and evaluate predictive/prescriptive models, including validation and monitoring.
• Knowledge of data governance, metadata management, data quality, security and privacy.
• Familiarity with version control, CI/CD, Git and JIRA.
• Ability to assess vendor solutions and integrate third-party platforms into data workflows.

Responsibilities

• Lead operations of the data platform including data lake/warehouse ingestion, processing, and monitoring.
• Guide design and development of ETL/ELT pipelines across multiple sources.
• Engage with stakeholders to translate business requirements into technical specifications.
• Ensure data quality, cleansing, validation, and integrity across datasets.
• Deliver dashboards, reports and analytics aligned with organizational goals.
• Oversee data reliability, security and compliance in collaboration with ITS Cybersecurity.
• Provide technical direction on model development and monitoring (predictive/prescriptive).
• Lead cross-functional collaboration with academic, corporate and ITS teams.
• Monitor and improve model performance and data accuracy.
• Promote innovation in analytics using ML, segmentation and visualization.
• Set standards for coding/tools usage (Python, JS, SQL) and reproducibility.
• Act as Analytics SME to guide stakeholders on advanced reports and analytics outputs.

Skills

Data governance

ETL/ELT pipelines

SQL

Python

Cloud platforms

Data lakes/warehouses/marts

Machine learning

Stakeholder management

Project leadership

Distributed computing (Spark/Hadoop)

Education

Bachelor's degree in CS/Data Science/IT

Master's degree (advantage)

Tools

AWS Glue

Athena

S3

Redshift

Apache Spark

Hadoop

Git

JIRA

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 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.
• 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.