The Data Business Analyst will act as a critical Subject Matter Expert (SME) and trusted interface between Maybank's Business Units, the Group Chief Data Office (GCDO), and Group Technology. Positioned within the AI/ML Center of Excellence (COE) and DDAD, you will own the end-to-end delivery of data requirements, data mapping, and demand management. You will serve as the primary Point of Contact (PIC) for data queries across various banking domains (Retail, Global Banking, Compliance, Risk, Finance, etc.), translating complex business needs into robust data solutions.
Key Responsibilities
1. Data SME & Business Support
- Act as the dedicated PIC for data-related queries from business users across multiple banking domains.
- Support business users in identifying and leveraging the right data elements, datasets, and data products for their specific use cases.
- Translate and explain technical data definitions, lineage, and usage into business-friendly language.
- Investigate and resolve data queries by working across various data platforms, products, and source systems.
2. Business Requirements & Data Mapping
- Elicit, analyze, and document comprehensive, data-focused business requirements.
- Translate business requirements into logical and physical data mappings, clearly detailing source systems, tables, columns, and data attributes.
- Collaborate closely with Group Technology and data engineering teams to ensure precise interpretation and implementation of requirements.
- Facilitate requirement walkthroughs, clarification sessions, and deep-dive technical data discussions.
3. Business Glossary & Metadata Documentation
- Create and maintain standardized business glossary definitions, data element descriptions, and domain terminology.
- Bridge the gap between business definitions and technical implementations to ensure enterprise-wide alignment.
- Ensure all data documentation is kept current, organized, and reusable across different teams and initiatives.
4. Enterprise Data Platform & Product Support
- Provide SME support for enterprise data platforms and data products, including Data Warehouses (DWH), Data Lakes, and Feature Stores.
- Support data onboarding, enhancements, and consumption use cases across the bank.
- Assist with User Acceptance Testing (UAT) planning and execution, validating that delivered data strictly aligns with business expectations.
- Partner with platform and engineering teams to troubleshoot and resolve data quality or platform issues.
5. Stakeholder & Demand Management
- Proactively manage the data demand intake process and prioritization pipeline for assigned business domains.
- Provide regular, structured updates to stakeholders regarding requirement status, technical dependencies, and delivery risks.
- Support broader data demand and asset management initiatives driven by the GCDO.
Key Initiatives You Will Support:
- Enterprise Data Platforms (DWH, Data Lakes, Feature Stores).
- Data Asset & Demand Management (Requirements, documentation, UAT).
- Feature Store and advanced data product enablement.
- GenAI Initiatives: Ensuring data readiness, gathering requirements, and supporting UAT for generative AI use cases.
- Driving adoption of analytics and data tools (e.g., Oracle Analytics Server (OAS), Enterprise Data Services Platform (EDSP)) across business units.
Qualifications & Skills
Experience
- 3 to 7 years of solid experience as a Business Analyst or Data Business Analyst.
- Prior banking or financial services experience is strongly preferred.
- Hands-on experience working directly within enterprise data platforms or advanced analytics environments.
Technical & Data Skills
- Strong foundational understanding of data concepts, including data models, datasets, and data lineage.
- Working knowledge of SQL for basic data exploration, querying, and validation.
- Proven experience mapping business requirements down to physical system tables and columns.
- Familiarity with data platforms and tools such as Power BI, OAS, Denodo, or Feature Stores is highly advantageous.
Soft Skills
- Strong analytical mind with a structured approach to problem-solving.
- Excellent communication skills, with a proven ability to explain complex data concepts to non-technical stakeholders.
- Comfortable owning data queries end-to-end and acting as a central point of contact.
- Exceptionally detail-oriented, organized, and proactive.
- Ability to collaborate effectively across multiple stakeholder groups in a fast-paced, matrixed environment.
