Enterprise Data Management Specialist

• *Proposed seniority**: Senior specialist

• *Recommended experience:** 8 years

• *Client environment**: Banking client

Location, employment type, contract duration and reporting line: To be confirmed

• *Role purpose**

Deliver reliable, discoverable and well\-governed enterprise data by combining data engineering with metadata, reference and master data, data modelling and data quality management. Work with business and technology teams to develop trusted data assets and support effective decision\-making across the banking client.

• *Key responsibilities**

· **Data discovery and asset management.** Discover and assess data ecosystems; establish and maintain data asset registers and associated data profiles, including ownership, usage and quality information.

· **Data engineering and integration.** Design, build and maintain data pipelines, data warehousing solutions and integrations. Implement validation, monitoring and error handling to support reliable data delivery.

· **Reference and master data.** Define, create, maintain and manage reference and master data across data domains and products. Establish standards, ownership, stewardship and processes for controlled data changes.

· **Metadata and taxonomy.** Define, document and maintain business, operational and technical metadata. Develop data taxonomies, business glossaries and lineage documentation in collaboration with domain stakeholders.

· **Enterprise data modelling.** Design relational, dimensional and NoSQL data models. Develop conceptual, logical and physical models aligned with the enterprise data model and agreed modelling standards.

· **Data quality management.** Define data quality rules and implementation requirements for business use cases. Execute profiling, validation, issue management, root\-cause analysis and remediation tracking to improve data quality maturity.

· **Standards and governance.** Document and implement data management standards, frameworks, guidelines, processes and procedures. Maintain evidence of adoption and support governance reviews.

· **Stakeholder engagement.** Build effective relationships with data owners, stewards, business users, architects and engineering teams. Promote consistent reference and master data practices across the organisation.

• *Qualifications and experience**

· Bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering or a related discipline, or equivalent relevant experience.

· Recommended 6–8 years of relevant experience in enterprise data management, with practical delivery across data engineering and governance.

· Demonstrated experience with metadata management, reference and master data, data modelling and data quality improvement.

· Experience working across multiple data domains and translating business requirements into technical specifications and reusable data assets.

· Banking, financial services, central banking or another regulated enterprise environment is advantageous.

• *Essential technical capabilities**

· Strong SQL skills for querying, profiling, reconciliation and validation.

· Hands\-on ETL/ELT development, data integration and data warehousing experience.

· Practical understanding of relational databases, dimensional modelling and NoSQL modelling approaches.

· Experience with data catalogues, business glossaries, metadata repositories and lineage documentation.

· Ability to implement reference and master data controls, data quality rules, scorecards and remediation workflows.

· Ability to document data standards, source\-to\-target mappings, data models and implementation requirements.

• *Preferred capabilities**

· Python or an equivalent scripting language for automation and data validation.

· Experience with enterprise data management platforms and modelling tools; specific products should match the client technology environment.

· Knowledge of DAMA\-DMBOK principles; CDMP or a comparable data management certification is advantageous.

· Experience contributing to data governance forums, maturity assessments and enterprise data architecture initiatives.

• *Core competencies**

Analytical problem\-solving; attention to detail; clear technical and business communication; stakeholder collaboration; ownership of delivery; and disciplined documentation.

• *Expected outcomes**

Maintained data asset registers and metadata; reliable pipelines and integrations; governed reference and master datasets; approved data models and taxonomies; documented quality rules and remediation plans; and measurable improvements in data quality and standards adoption.

Work Location: In person