Senior Solution Architect, Data & AI

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Job Summary

The Senior Solutions Architect, Data leads the technical response on data platform opportunities within CDW UK's Data & AI practice. Working alongside the wider solution sales organisation they design modern data platforms, whether unified or federated, that let customers collect, store, integrate, process, govern and analyse their data. The result is a trusted, secure, scalable, and cost-efficient foundation for data-driven decisions and AI. Modernisation is at the heart of the role: helping customers move away from legacy data warehouses, databases and fragmented data estates towards modern lakehouse, warehouse and federated designs on platforms such as Databricks, Snowflake, Microsoft Fabric and Starburst.

In a pre-sales capacity, there is a demand for both technical and commercial awareness coupled with an ability to communicate with a wide range of customer stakeholders.. The role qualifies the technical requirement on opportunities, leads discovery with business, data and IT stakeholders, and turns customer goals into technical valid, commercially credible designs covering:

• Data Ingestion and Integration
• Storage and management
• Processing and Transformation
• Design of the Semantic Layer
• Data Sharing
• Delivery of AI-ready data

Governance, security, observability and cost are designed in at every stage.

They work closely with Solution Sales Specialists, Partners and their fellow architects in associated domain specialistations. Physical storage, Infrastructure and Cloud platforms are handed to Hybrid Platforms Practice, with Data Protection and Governance coming from our Security Practice.

The role also has input into the Data Platforms go-to-market by creating reusable reference architectures, modernisation assessments, demonstrations and packaged offerings. The candidate also supports sales enablement and the development of colleagues across the practice

What you will do

• Enterprise Data Strategy - Advise customers on data strategy and target-state architecture, linking business priorities to a practical data platform roadmap that supports analytics, operational decisions and AI.
• Data Estate Modernisation - Lead the technical case for modernising legacy data warehouses, databases and fragmented data estates. Assess the current state, define migration approaches and phased roadmaps, and identify risks, dependencies and quick wins.
• Data Architecture & Modelling - Design end-to-end data platform architectures, including lakehouse, warehouse, federated and data-product approaches. Apply appropriate modelling patterns such as medallion, dimensional and domain-oriented designs.
• Data Integration & Engineering - Design ingestion and integration patterns across batch, streaming and change data capture (CDC). Define ETL/ELT, transformation and orchestration approaches suited to the customer's scale, skills and operating model.
• Data Storage & Management - Design the logical storage layer, including open table formats, database selection, partitioning, retention and lifecycle management. Work with Hybrid Platforms architects, who own physical storage and infrastructure.
• Data Governance, Discovery & Quality - Design governance into every solution, covering cataloguing, lineage, ownership, business classification, data quality and access policy. Work with Security architects, who own data protection and data loss prevention.
• Semantic Layer & Analytics - Design the semantic and business-model layer that provides consistent, trusted definitions for BI, self-service analytics and AI. Guide analytics and visualisation choices without building dashboards or reports.
• AI-Ready Data - Make sure data platforms provide the quality, context, access and governance that AI and machine learning need. Work with the Principal AI Architect on AI use cases that depend on the data foundation.
• Security, Observability & FinOps by Design - Build security, privacy, monitoring, reliability and cost management into every layer of the architecture. Give customers a clear view of consumption-based platform costs and how to optimise them.
• Independent Platform Viewpoint - Give customers impartial, evidence-based advice across platforms such as Databricks, Snowflake, Microsoft Fabric and Starburst. Recommend the right fit for their requirements, existing investments and skills, rather than defaulting to one vendor.
• Opportunity Qualification & Discovery - Work with Solution Sales Specialists, Business Development Managers (BDMs) and partners to qualify opportunities, lead discovery workshops, and capture business and technical requirements. Keep CRM information accurate and up to date to support forecasting and pipeline management.
• Demonstrations & Proofs of Value - Run customer-facing demonstrations and help scope proofs of value, with clear success criteria that show the business impact of modern data platforms.
• Documentation & Commercials - Produce high-quality solution designs, high-level designs (HLDs), request for proposal (RFP) responses, business cases, total cost of ownership (TCO) and return on investment (ROI) models, and technical input to statements of work (SoWs), with clearly defined scope, assumptions and dependencies.
• Technical Collaboration & Handover - Act as the data platforms link between the Principal AI Architect, the Hybrid Platforms, Cloud and Security architects, and the delivery teams. Collaborate to ensure solutions come together end to end and hand over cleanly to implementation.
• Offering Development - Help create and develop the Data Platforms go-to-market, including reference architectures, modernisation assessments, demonstrations and packaged offerings. Work with the team, practice leadership and the Office of the CTO.
• Partner Engagement - Build strong technical relationships with data platform partners to stay current on roadmaps and capabilities, while using that knowledge as an independent advisor to customers.
• Continuous Learning & Accreditation - Keep expert knowledge up to date through hands-on lab time, research and training. Maintain relevant accreditations in at least one core data platform ecosystem.
• Market Awareness & Thought Leadership - Track emerging data platform trends and share practical insights internally and externally, raising CDW's profile as a trusted data and AI advisor

What we expect of you

• Typically 5+ years in data architecture, data engineering or analytics platforms, including at least 3 years in a customer-facing pre-sales, consulting or solution architecture role.
• A proven track record of designing enterprise data platforms, with clear examples of your own architectural decisions and the trade-offs behind them.
• Demonstrable experience leading data estate modernisation, such as moving from legacy data warehouses, on-premises databases or Hadoop to modern cloud or hybrid data platforms.
• Deep expertise in at least one modern data platform (Databricks, Snowflake or Microsoft Fabric), plus a working knowledge of the alternatives that lets you give impartial advice on each.
• A strong understanding of data architecture patterns, including lakehouse, data warehouse, federated/virtualised data (e.g. Starburst/Trino), data mesh and data products.
• Solid knowledge of data modelling, including dimensional, medallion and domain-oriented approaches, and of designing semantic layers for consistent business definitions.