Data Architect

The Role:

At Canopius, our delivery teams are responsible for ensuring that business users can effectively harness data insights to drive strategic decision making. Our data strategy is centred around an enterprise Lakehouse platform on Databricks, avoiding fragmented, ungoverned silos on legacy technologies that hamper creativity and scalability.

The Data Architect owns the end-to-end design and evolution of our enterprise data architecture, defining how data is stored, integrated, secured and managed across solutions. The ideal candidate will be an experienced data architect who is excited by the opportunity to shape enterprise data standards, drive data re-use and interoperability across the organisation, and leverage modern technologies to simplify the data estate and unlock greater value from data.

You should be able to understand business problems and translate them into robust, efficient and well-governed data designs, including conceptual, logical and physical data models, while owning and maintaining data modelling standards and architecture principles. You should be comfortable collaborating in project teams and cross-departmental squads including business analysts, data analysts, developers and solution architects to deliver business solutions. The role also owns the end-to-end data architecture of our enterprise Lakehouse platform on Databricks, including data modelling, storage patterns, integration approaches, data sharing patterns and governance guardrails. In addition, the role is responsible for shaping the future-state data architecture, supporting transition roadmaps, and driving simplification through the rationalisation of legacy data assets and technical debt.

The candidate should be a practical, delivery-focused architect with strong interpersonal skills and a proven track record of producing data architecture artefacts, shaping solution designs and working closely with delivery teams to implement robust data solutions.

This is a hybrid role working 2-3 days a week in our Manchester office.

Responsibilities will include:

• Define conceptual, logical and physical data models, information models and associated data dictionaries to meet enterprise and delivery-specific requirements.

• Own the end-to-end architecture of the enterprise Lakehouse platform on Databricks, including data modelling, storage layers, processing patterns, integration approaches and data sharing standards.

• Define and evolve the future-state data architecture and associated transition roadmaps, identifying opportunities to simplify the landscape through rationalisation of legacy data assets, duplicated capabilities and technical debt.

• Define data architecture principles, guardrails, standards and reusable patterns, ensuring alignment to enterprise naming conventions, canonical business definitions and domain boundaries.

• Define and evolve insurance data models and standards across underwriting, policy, claims, finance, actuarial and regulatory use cases.

• Define security, privacy and regulatory requirements for data architecture, including data classification, access control, retention, lineage, metadata and information quality controls.

• Collaborate with business stakeholders, delivery teams and solution architects to capture data requirements, shape solution options and communicate risks, issues and trade-offs.

• Partner closely with data engineers and solution teams to ensure architecture designs can be implemented scaled and aligned to modern engineering patterns and best practices.

• Define data integration solutions and source to target mappings between business applications as well as between applications and the data platform, to ensure that data is synchronised promptly as required and remains in line with relevant data models and data quality controls.

• Produce and maintain the reference data catalogue, ensuring that reference data is understood, fit for purpose, aligned with enterprise standards and appropriately versioned and maintained.

• Review and contribute to solution design artefacts in collaboration with Data Governance and Compliance to ensure that data quality, governance, security and privacy requirements are addressed appropriately, and contribute to architecture decision forums and governance processes for design assurance and sign-off.

• Specify key data fields, data capture rules, validation rules and reference data requirements required to support critical business processes and data solutions.

• Own integration and data sharing standards, including interface specifications such as JSON and REST APIs, ensuring that structures support interoperability, decoupling and future-proofing.

• Represent information in forms appropriate to the solution context, including UML-friendly models, JSON structures and relational models, and evolve ways of working to improve maintainability and re-use.

• Design for performance, scalability, resilience and cost efficiency across the data platform and associated data services.

• Mentor delivery team members on best practices in data architecture and support requirement gathering across data flows, transformations, validation rules and non-functional requirements.

Further Relevant Skills and Experience

• Proven track record of delivering data architecture artefacts for a variety of solutions.

• Experience of creating information models for analytical and operational use cases.

• Proficient in modelling and architecture tools such as Erwin and SparxEA, with the ability to apply sound architecture principles and modelling practices regardless of tooling.

• Strong knowledge of SQL, relational modelling and JSON.

• Strong experience designing data architectures for modern cloud data platforms, including Databricks and Azure.

• Experience defining architecture principles, reusable patterns and guardrails rather than relying solely on tool-specific approaches.

• Prior experience of data model and data management design within delivery projects for underwriting, policy administration or claims applications.

• Experience defining metadata, lineage and data quality monitoring requirements within enterprise data platforms.

• Understanding of data security, privacy, access control and regulatory considerations relevant to enterprise and insurance data.

• Experience designing for performance, scalability, resilience and cost efficiency across data platforms and data services.

• Proven experience of stakeholder management skills at all levels, with the ability to influence senior business and technology stakeholders.

• Good communication and presentation skills; able to convey ideas, information and concepts effectively with business stakeholders and team members.

• Must be able to work independently on certain tasks, as well as in team environments.

• Experience working in cross-functional teams and mentoring project team members.

• Understanding of London or Lloyd’s Market insurance conceptual data models and associated business data standards would be beneficial.

• Experience using enterprise architecture tooling such as BizzdesignHorizzonwould be beneficial.