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Business Analyst - MDM Applications

CognizantAbu Dhabi Emirate, UAEYesterdayEntry
Entryfulltime

Skills

Requirements GatheringBusiness Process MappingStakeholder AnalysisUMLBPMNAgile
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About This Role

Role Summary

The Business Analyst (BA) for on-premise MDM applications acts as the bridge between business stakeholders, data owners/stewards, and technical delivery teams to define and deliver MDM capabilities. The BA leads discovery and requirements definition for master data domains and processes, documents business rules for matching/merging and survivorship, supports data quality and governance alignment (including glossary and metadata needs), and ensures end-to-end traceability through testing and deployment readiness.

Key Responsibilities

  • Conduct stakeholder workshops to gather and document business, functional, and non-functional requirements for on-prem MDM enhancements and BAU changes (process flows, CRUD, stewardship workflows).
  • Define, document, and obtain sign-off for MDM business rules including match/merge rules, survivorship criteria, golden record attributes, and exception handling; support periodic review of rules with SMEs.
  • Create and maintain source-to-target mappings, data lineage documentation, and business rule catalogs for mastered entities and integrations (feeds to/from source systems).
  • Partner with Data Quality and technical teams to define DQ requirements and controls (validation rules, thresholds, exception categories) and ensure traceability to implemented rules (e.g., in IDQ/OEDQ) and monitoring.
  • Support governance alignment by defining business glossary needs (data terms/definitions, critical data elements) and metadata requirements for cataloging and lineage (Axon/EDC alignment where applicable).
  • Coordinate data sourcing and ingestion readiness: validate source system attributes, clarify transformation logic, agree reconciliation/controls, and ensure completeness for DQ monitoring.
  • Support testing and deployment: derive acceptance criteria, review test cases, support UAT, validate data outcomes (match results, survivorship behavior), and manage defects/clarifications through release cycles.
  • Produce high-quality documentation (BRD/FRD, user stories, process maps, SOPs, runbooks) and ensure requirements traceability from discovery to production sign-off.

Required Skills & Experience

  • 5+ years of Business Analysis experience in data-centric programs (MDM, reference data, data quality, data governance, or data integration).
  • Strong understanding of MDM concepts: master data domains, golden record, match/merge, survivorship, hierarchy/reference data, and stewardship workflows.
  • Hands-on experience creating BRD/FRD, user stories, process maps, and source-to-target mappings; strong requirements traceability and sign-off management.
  • Strong analytical skills with proficiency in SQL and Excel for data analysis, validation, and reconciliation support.
  • Excellent stakeholder management and communication skills; ability to translate business needs into clear, testable requirements for technical teams.

Preferred / Nice-to-Have

  • Experience working with on-prem MDM application stacks (e.g., Siebel UCM/Customer Hub) and familiarity with match-rule and survivorship configuration concepts.
  • Exposure to Data Governance and metadata practices including business glossary and catalog/lineage concepts (e.g., Axon/EDC) and linkage to DQ tooling (e.g., IDQ).
  • Experience in Agile delivery (user stories, backlog grooming, sprint ceremonies) and tools such as JIRA/Confluence.

Typical Deliverables / Outcomes

  • Approved BRD/FRD or user story set with acceptance criteria and full requirements traceability for MDM releases.
  • Documented and signed-off match/merge and survivorship rules, including exception scenarios and stewardship impacts.
  • Source-to-target mappings, reconciliation/validation checkpoints, and data lineage documentation supporting ingestion and monitoring needs.
  • UAT support artifacts: test scenario coverage, defect triage notes, and production readiness sign-off aligned to expected data outcomes.

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