Senior Solution Architect, Data & AI

Role summary

Customer strategy and modernisation

• Advise customers on data strategy and target-state architecture, translating business priorities into practical platform roadmaps.
• Lead modernisation of legacy warehouses, databases and fragmented data estates through current-state assessment, migration approaches and phased roadmaps.
• Identify modernisation risks, dependencies and quick wins.

Architecture and engineering

• Design lakehouse, warehouse, federated and data-product architectures using appropriate medallion, dimensional and domain-oriented modelling patterns.
• Design batch, streaming and change data capture ingestion and integration, plus ETL/ELT, transformation and orchestration approaches.
• Design logical storage, including open table formats, database selection, partitioning, retention and lifecycle management.
• Design semantic and business-model layers that provide trusted definitions for BI, self-service analytics and AI.

Governance, security and AI readiness

• Build cataloguing, lineage, ownership, classification, data quality and access policy into solutions.
• Ensure data platforms provide the quality, context, access and governance needed by AI and machine learning.
• Embed security, privacy, monitoring, reliability and cost management, and explain platform consumption costs and optimisation.

Pre-sales and practice contribution

• Qualify technical requirements and lead discovery with business, data and IT stakeholders.
• Create technically valid, commercially credible designs and provide impartial, evidence-based platform recommendations.
• Contribute reference architectures, modernisation assessments, demonstrations and packaged go-to-market offerings.
• Support sales enablement and the development of colleagues across the practice.

Platforms and architecture domains

• Platforms named include Databricks, Snowflake, Microsoft Fabric and Starburst.
• Data platform areas include ingestion, integration, storage, processing, transformation, semantic design, data sharing and AI-ready data.
• Physical storage and infrastructure are handled by the Hybrid Platforms practice; data protection and governance are supported by the Security practice.

Candidate profile

• Bring technical and commercial awareness to pre-sales and communicate with a wide range of customer stakeholders.
• Lead customer discovery and translate business goals into technically sound, commercially credible designs.
• Recommend platforms based on customer requirements, existing investments and skills rather than defaulting to one vendor.