Senior Business Analyst (Data)

Position Overview

As a Senior Business Analyst (Data), you will play a pivotal role in the delivery of an Enterprise Data Warehouse (EDW) program for a supported business. This remote, full-time position requires deep expertise in data analysis, modelling, and reconciliation, with a strong emphasis on regulatory and statutory reporting. You will be engaged across multiple releases, from initial requirements gathering to final reconciliation sign-off, ensuring the highest standards of data accuracy and compliance.

Key Responsibilities

Requirements Analysis & BRD

• Elicit, analyze, and document business, functional, and non-functional data requirements from business users, data owners, and regulatory stakeholders.
• Author and maintain Business Requirements Documents (BRDs), functional specifications, and data requirement specifications, securing formal sign-off at each stage gate.
• Decompose requirements into source-to-target mapping (STTM) specifications, including clear transformation rules, business logic, and acceptance criteria.
• Maintain end-to-end requirements traceability from business need through data model, build, and test.

Data Modelling

• Define and review conceptual, logical, and physical data models for staging, conformed, and consumption layers, collaborating with data architects.
• Apply recognized modelling techniques (dimensional/Kimball, 3NF/Inmon, and Data Vault) and select appropriate patterns for each layer.
• Specify integrated customer models (e.g., Single Customer View), conformed dimensions, slowly-changing-dimension strategies, grain, keys, and historization rules.
• Review physical models against the target MPP platform for distribution, partitioning, and performance.

Data Warehouse Migration & Mapping

• Analyze legacy warehouse structures and transformation logic, specifying their functionally-equivalent target on the new platform.
• Produce source-to-target mappings for ELT, including data type conversions, code-set harmonization, and derivation rules.
• Identify and document gaps, ambiguities, and scope boundaries between legacy behavior and target design.

Data Reconciliation & Validation

• Design and own the data reconciliation strategy between legacy and target, and between source and warehouse (e.g., row counts, control totals, balances, hash/checksum, business-rule reconciliation).
• Define reconciliation rules, tolerances, and exception-handling, leading reconciliation efforts during SIT, UAT, and parallel run.
• Investigate, triage, and drive resolution of breaks to root cause, obtaining business sign-off on reconciliation results before go-live.

Data Governance & Quality

• Define data quality rules, profiling criteria, and data-quality KPIs; collaborate with the governance team on business glossary, data dictionary, lineage, and metadata.
• Support data ownership, classification, and retention decisions, ensuring regulatory data requirements are reflected in the models.

Testing Support (SIT / UAT)

• Translate requirements into test conditions and data-centric test cases; review test coverage and defect trends.
• Coordinate UAT with business users, manage data-related defects, and confirm requirements are met before sign-off.

Stakeholder Management

• Act as the bridge between business, regulatory, architecture, engineering (including platform and integration vendors), and the PMO.
• Facilitate workshops, resolve conflicting requirements, and communicate data decisions and their impact to both technical and non-technical audiences.

Required Qualifications & Experience

• Minimum 10 years of business analysis experience, with the majority focused on enterprise data warehouse, data migration, or data-platform programs.
• Bachelor's degree in Computer Science, Information Systems, Data/Analytics, Engineering, Finance, or a related discipline.
• Demonstrable, hands-on experience across three mandatory areas:
  • Enterprise Data Warehousing: Delivered requirements and mappings on at least one large EDW build or migration, ideally on an MPP platform (e.g., Netezza, Teradata, GaussDB/DWS, Snowflake, BigQuery, Redshift).
  • Data Modelling: Produced or governed logical/physical models using dimensional, 3NF, and/or Data Vault techniques for integration and consumption layers.
  • Data Reconciliation: Owned source-to-target and legacy-to-target reconciliation on a migration or parallel run, including break analysis and business sign-off.
• Strong, practical SQL skills, capable of profiling data, validating mappings, and building reconciliation queries independently against large datasets.
• Proven experience writing Business Requirements Documents (BRDs), functional specifications, and source-to-target mapping documents that engineering teams can build from without rework.
• Experience across the full SDLC on data programs: analysis, design, build, SIT, UAT, parallel run, and go-live.
• Experience in banking or financial services, working with regulated data.
• Excellent written and spoken English; working proficiency in Bahasa Malaysia is an advantage for local stakeholder engagement.

Technical Skills & Tools

• Data Warehouse Platforms: MPP/columnar warehouses such as Netezza, Teradata, GaussDB (DWS), Snowflake, BigQuery, Redshift, or equivalent.
• Data Modelling: Dimensional (Kimball), Normalized (Inmon/3NF), and Data Vault 2.0; experience with modelling tools like erwin, ER/Studio, SQLDBM, or PowerDesigner.
• SQL & Data Manipulation: Advanced SQL for profiling, validation, and reconciliation; comfort with large-volume query tuning concepts (distribution, partitioning, indexing).
• ETL/ELT & Integration: Understanding of ELT push-down patterns and at least one enterprise integration/orchestration tool (e.g., Informatica, DataStage, Talend, Sunline DataMind, or a native cloud service).
• Data Quality & Governance: Data profiling and DQ tooling, business glossary/data catalogue, metadata, and lineage concepts.
• Reconciliation & Analysis: Reconciliation frameworks, control-total and checksum techniques; advanced Excel; Python or scripting for data validation is a plus.
• BI & Reporting: Awareness of downstream consumption tools such as Power BI, Tableau, Cognos, or MicroStrategy.
• Delivery Tooling: Requirements and ALM tools (JIRA, Confluence, Azure DevOps), and schedule awareness (MS Project / plan-driven delivery).

Domain Knowledge

• Banking Data Domains: Core banking, deposits/CASA, loans and financing, cards, trade finance, treasury, general ledger, customer, and party data.
• Customer / Party: Single-customer-view and group customer information concepts – identity resolution, deduplication, householding, and relationship hierarchies.
• Regulatory Reporting (Malaysia): Familiarity with local regulatory reporting obligations, including those for Bank Negara Malaysia (BNM) and PIDM, and the data and lineage controls they demand; prior exposure to statutory/regulatory submission data is highly valued.
• Risk & Finance: Awareness of credit, market, and liquidity risk data, and finance/GL reconciliation, is an advantage.
• Controls & Compliance: Data privacy, classification, and retention expectations for regulated financial data.

Core Competencies

• Rigorous and detail-oriented, with a strong bias toward traceability and evidence – ensuring every requirement and reconciliation result is defensible and sourced.
• Structured problem-solver able to break ambiguous business needs into precise, buildable data specifications.
• Confident facilitator and communicator who can engage effectively with both senior business stakeholders and technical teams.
• Able to work independently and lead junior analysts; comfortable owning a workstream end-to-end.
• Pragmatic under delivery pressure – manages competing priorities across parallel releases without compromising quality.
• Flags risks, assumptions, and gaps early rather than iterating on flawed approaches.

Preferred Qualifications

• Prior work on a Netezza-to-GaussDB/DWS or comparable MPP-to-MPP migration.
• Experience with ELT re-engineering (moving transformation logic into the warehouse).
• Hands-on experience with Data Vault 2.0 for the integration layer.
• Exposure to Sunline DataMind, Huawei data stack, or similar enterprise data-governance platforms.
• Professional certification: CBAP / CCBA (IIBA), CDMP (DAMA), TOGAF, or a recognized cloud-data certification.
• Experience delivering local regulatory data programs.
• Experience in a multi-vendor delivery model (systems integrator plus platform and infrastructure partners).