Lead Data Solution Architect

As the Lead Data Solution Architect, you will take ownership of defining the approach for assessing source systems, profiling data, producing target models, and creating reusable source-to-target mappings. Working closely with business stakeholders, engineering teams, data owners, and governance functions, you will establish standards and patterns that can be scaled across future delivery workstreams.

You will play a pivotal role in ensuring data assets are understood, documented, and architecturally aligned before engineering teams commence implementation activities., * Establish and maintain a structured source system assessment and discovery framework.

• Create and manage an inventory of source systems, including ownership, availability, access requirements, dependencies, and data characteristics.
• Conduct hands-on data profiling and source system analysis to validate suitability and readiness for integration.
• Engage with business and technical stakeholders to understand source system behaviours, dependencies, and challenges.
• Define target data models aligned to enterprise data standards and architecture principles.
• Produce detailed source-to-target mappings and transformation requirements.
• Create and manage implementation backlogs within Jira, including integration decisions, dependencies, user stories, and acceptance criteria.
• Collaborate closely with engineering teams to support implementation activities and resolve architectural challenges.
• Establish repeatable modelling, mapping, and integration patterns that can be adopted across future data initiatives.
• Produce clear architectural documentation and implementation guidance for wider delivery teams.

• Proven experience operating as a Data Architect, Solution Architect, or Lead Data Architect within large enterprise environments.
• Strong background in source-to-target mapping, data discovery, data profiling, and integration architecture.
• Experience designing enterprise data models and supporting large-scale data transformation programmes.
• Strong understanding of modern data platform architectures, including data lakehouse and medallion architecture approaches.
• Experience producing logical and physical data models aligned to enterprise architecture standards.
• Hands-on experience working with Jira to manage architecture backlogs, dependencies, requirements, and delivery planning.
• Ability to work effectively with business stakeholders, system owners, architects, engineers, and governance teams.
• Strong understanding of data lineage, metadata, data quality, and integration principles.
• Experience operating within regulated industries such as financial services, insurance, banking, or other highly governed environments.
• Excellent stakeholder management, communication, and documentation skills.

Desirable Experience

• Experience working with cloud-based data platforms and enterprise-scale data modernisation programmes.
• Knowledge of data governance frameworks, critical data elements, metadata management, and data cataloguing.
• Experience with legacy data warehouse, ETL, and database technologies.
• Understanding of data migration and legacy platform modernisation initiatives.
• Exposure to enterprise semantic layers, data products, and modern analytics architectures.
• Experience supporting Agile delivery teams and domain-aligned data operating models.

Skills

• Physical
• Logical
• Conceptual
• Data Discovery
• Data Solution Architect
• Data Architecture
• Data Modelling
• Data Profiling
• Data Transformation
• Source-to-target

A large global consultancy is seeking an experienced Lead Data Solution Architect to support a major enterprise-wide data transformation programme within a complex and regulated environment.

This is a key architecture role focused on establishing the source-to-target data foundations required to enable future data platform delivery. You will lead source discovery, data profiling, modelling, and mapping activities, creating a repeatable framework that can be adopted across a broad data landscape.

The position is focused on laying the architectural groundwork and implementation patterns required for long-term success, rather than delivering a complete transformation of the data estate.