Senior Business Systems & Data Analyst
- Compensation
- Not disclosed
- Location
- Damansara, Selangor
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data analytics
- Posted
Skills
Full description
Role Overview
This hybrid role bridges Business & Systems Analysis, EMS and Enterprise CRM Architecture, and Advanced Data Analytics / Business Intelligence (BI). You will be responsible for translating institutional needs into automated workflows, managing core enterprise architectures, and turning raw data into strategic, prescriptive insights for university leadership.
Key Responsibilities1. Requirements Gathering & Systems Analysis
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Stakeholder Workshops: Lead requirement gathering sessions with Sales, Marketing, Support, and Academic departments to translate vague needs into technical requirements.
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Functional Specifications: Define and document functional specifications, user stories, and comprehensive acceptance criteria for system enhancements.
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Gap Analysis: Evaluate existing CRM, EMS, and LMS functionalities against evolving business objectives to recommend upgrades or integrations
2. CRM Architecture & System Administration
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Lifecycle Management: Manage the complete lifecycle of the enterprise CRM platform (e.g., Salesforce, Microsoft Dynamics, HubSpot), including user access, custom objects, and custom permission sets.
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Workflow Automation: Design and configure complex workflow automations, validation rules, processes, and flows to streamline sales, marketing, and university operations.
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System Integrations: Oversee API integrations between the CRM, EMS, third-party tools, ERP systems, billing systems, and external marketing databases.
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Troubleshooting: Diagnose and resolve advanced technical CRM platform bugs, system performance lags, and data sync errors.
3. Business Process Optimization
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Process Mapping: Analyze current-state operations to identify operational bottlenecks and design future-state automated system workflows.
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User Acceptance Testing (UAT): Coordinate UAT sessions, defining test scripts and ensuring system outputs perfectly match initial analysis requirements.
4. Data Engineering & Analytics Architecture
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Data Pipelines: Design and implement end-to-end data pipelines in collaboration with data engineering teams to ensure clean, structured data.
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Design data models and schema mappings that align with broader enterprise architectures and reporting requirements.