Overview
The Data Integration Specialist role involves working with a leading digital consultancy to design secure and reusable data integration architectures. The contractor will collaborate with multidisciplinary teams to support the development of a complex data integration capability, focusing on data semantics, standards, and securing customer locations while ensuring high data quality and compliance.
Responsibilities
- Design secure, reusable data integration architectures across heterogeneous source systems.
- Define and maintain common/canonical data models and Information Exchange Standards (IES) mapping approaches.
- Establish consistent data semantics, metadata, provenance, lineage, and data quality standards.
- Define API contracts covering search, retrieval, pagination, versioning, and error handling.
- Develop integration patterns separating common services from source-specific adapters.
- Design authentication, authorisation, policy controls, encryption, and audit logging.
- Define monitoring, diagnostic, and resilience requirements for the integration layer.
- Produce architecture diagrams, information models, mapping specifications, and technical documentation.
Requirements
- Active SC and NPPV3 clearance – non-negotiable.
- Strong background as a Data Architect, Data Integration Architect, Information Architect, or Information/Data Modelling Architect.
- Experience with Information Exchange Standards (IES / IES Next), including source-to-target mapping and alignment to common information models.
- Strong understanding of data semantics, ontology, metadata, provenance, lineage, and data quality.
- Experience designing secure integration solutions across complex or legacy systems, including federated/distributed data architectures.
- Strong experience with RESTful APIs and OpenAPI, including versioning and contract management.
- Experience with authentication, authorisation, policy-based access controls, encryption, and auditability.
- Understanding of resilience patterns covering timeouts, source failures, partial results, and conflicting data.