Key Responsibilities
Enterprise Data Modelling
- Lead the design and review of enterprise data models across conceptual, logicaland physical layers.
- Establish and maintain enterprise data modelling standards and best practices.
- Develop scalable data structures that support business reporting, analytics and self-service data consumption.
- Ensure consistency and integration across multiple subject areas and business domains.
- Collaborate with business and technical stakeholders to align data structures with business requirements.
Areas of Expertise Required:
Dimensional Modelling
- Star Schema Design
- Snowflake Schema Design
- Fact Table Design
- Dimension Table Design
- Slowly Changing Dimensions (SCD)
Enterprise Modelling
- Conceptual Data Modelling
- Logical Data Modelling
- Physical Data Modelling
Data Warehouse & Analytics Design Review
- Review and assess existing data warehouse and data mart designs.
- Evaluate vendor-delivered data models and recommend improvements.
- Support data warehouse modernisation and transformation initiatives.
- Identify modelling gaps, redundancies and opportunities for optimisation.
- Ensure data models support future analytical and operational requirements.
- Contribute to target-state data architecture and modelling frameworks.
Modern Analytics Platform Enablement
Work closely with architecture, engineering and analytics teams to ensure data models are optimised for modern analytics platforms including:
Data Lakes
- Azure Data Lake Storage (ADLS)
- AWS S3
- Google Cloud Storage
Lakehouse Platforms
- Microsoft Fabric
- Databricks
- Snowflake
Data Warehouse Platforms
- Azure Synapse Analytics
- Snowflake
- BigQuery
- Amazon Redshift
The successful candidate should understand how modelling approaches differ across traditional data warehouses, data lakes and lakehouse environments.
Business Intelligence & Analytics Support
- Design data models that support effective analytics consumption.
- Partner with BI teams to improve semantic layer design.
- Enable self-service analytics capabilities.
- Support enterprise KPI and metric standardization.
- Optimise data structures for reporting performance.
Experience supporting tools such as:
- Power BI
- Tableau
- Qlik
- Looker
Data Governance & Data Quality
- Support enterprise data governance initiatives through effective modelling practices.
- Define and document business definitions, data lineage and metadata requirements.
- Promote data consistency, standardization and reusability.
- Support implementation of data quality and master data management initiatives.
- Collaborate with data owners and stewards to improve data integrity.
Exposure to the following tools is advantageous:
- Microsoft Purview
- Informatica
Performance & Optimisation
- Design models that support efficient query performance and scalability.
- Review and optimise:
- Data storage structures
- Partitioning strategies
- Data refresh performance
- Data loading efficiency
- Semantic model performance
- Identify:
- Data duplication
- Modelling inefficiencies
- Unnecessary transformations
- Technical debt
- Performance bottlenecks
Stakeholder Engagement
- Partner with business users, analytics teams, data engineers and solution architects to understand requirements and translate them into scalable data models.
- Facilitate data modelling workshops and design reviews.
- Present modelling recommendations and design decisions to stakeholders.
- Support delivery of assessment and improvement initiatives.
Typical deliverables include:
- Data Modelling Standards
- Conceptual, Logical and Physical Data Models
- Current-State Data Assessments
- Gap Analysis Reports
- Data Warehouse Design Reviews
- Data Dictionary and Metadata Documentation
- Modelling Recommendations
- Future-State Data Models
Required Qualifications
Experience
- 8+ years of experience in data warehousing, analytics and data modelling.
- Proven experience designing enterprise-scale data models.
- Experience supporting large-scale data warehouse or analytics platforms.
- Experience reviewing and improving existing data warehouse and data mart designs.
- Experience working with cross-functional business and technology teams.
Technical Skills - Must Have
Data Modelling
- Dimensional Modelling
- Star Schema
- Snowflake Schema
- Fact & Dimension Modelling
- Slowly Changing Dimensions
- Conceptual, Logical and Physical Data Modelling
Data Warehousing
- Enterprise Data Warehouse Design
- Data Mart Design
- Data Integration Concepts
- Analytics Data Structures
Cloud Analytics Platforms
- Microsoft Fabric, Databricks, Snowflake or equivalent modern analytics platforms
- Understanding of Data Lake and Lakehouse concepts
Business Intelligence
- Understanding of semantic modelling and analytics consumption patterns
- Experience supporting Power BI, Tableau, Qlik or Looker environments
Data Governance
- Data Lineage
- Metadata Management
- Data Quality Frameworks
- Master Data Management Concepts
Preferred Qualifications
- Experience in Financial Services, Banking, Insurance or other large enterprise environments.
- Experience with Azure Data ecosystem:
- Azure Data Lake
- Azure Synapse Analytics
- Azure Data Factory
- Microsoft Fabric
- Azure SQL
- Databricks
- Microsoft Purview
- Knowledge of modern data architecture and analytics design patterns.
- Experience working with external vendors and system integrators
Pay: RM8,000.00 - RM12,000.00 per month
Benefits:
- Flexible schedule
- Health insurance
- Opportunities for promotion
- Professional development
Work Location: In person