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.
Experience designing ingestion and integration patterns, including batch, streaming, change data capture (CDC), ETL/ELT and orchestration.
A good understanding of relational and non-relational databases, open table formats (e.g. Delta Lake, Apache Iceberg), and data storage and lifecycle design.
Practical knowledge of data governance, including cataloguing, lineage, data quality, ownership, classification and access policy, with tools such as Databricks Unity Catalog, Microsoft Purview or Snowflake Horizon.
An understanding of data security, privacy and compliance requirements (e.g. UK GDPR) and how they shape platform design.
The ability to model and explain the costs of consumption-based data platforms, and to build TCO and ROI cases customers can defend.
Enough hands-on ability in SQL and Python to build credible demonstrations and to test designs and proofs of value.
Experience leading discovery workshops and communicating clearly with both technical and business stakeholders, from data engineers to CDOs and CFOs.
The ability to produce high-quality solution designs, proposals and RFP responses with clearly defined scope, assumptions and dependencies.
Desirable
A degree or equivalent qualification in computer science, data or a related field. Equivalent experience is equally valued.
Relevant certifications, such as Databricks Certified Data Engineer Professional, SnowPro Advanced: Architect, or Microsoft Certified: Fabric Data Engineer Associate / Fabric Analytics Engineer Associate.
Experience with major cloud data services on Azure, AWS or Google Cloud.
Knowledge of streaming platforms such as Apache Kafka or cloud-native equivalents.
Familiarity with BI and semantic tooling, such as Power BI semantic models, or the Databricks/Snowflake semantic capabilities.
An understanding of how data platforms support AI and machine learning, including feature data, retrieval and data preparation for generative AI.
Experience of data observability and DataOps practices.
Experience in a channel, reseller or technology partner environment
Experience creating reusable offerings, reference architectures or assessments within a consulting or pre-sales practice.
Experience mentoring colleagues or leading small virtual teams
CDW is committed to being an AI-fluent organization We’re looking for people who bring curiosity, a learner’s mindset, and a willingness to engage with ever-evolving technology and tools. We value adopting AI as a partner, openness to experimentation, and a shared interest in learning together on AI. Our goal is to create a culture where AI enhances—not replaces—human creativity and decision-making. You don’t need to be an expert today; what matters is your readiness to explore, adapt, and grow with us as we integrate AI responsibly and effectively into our work.
Additionally, CDW is committed to fostering an equitable, transparent, and respectful hiring process for all applicants. During our application process, our goal is to understand your experience, strengths, skills, and qualifications. As an AI forward company, we see AI not just as a tool, but as a catalyst for new ways of thinking, creating, and communicating. We encourage candidates to embrace an AI mindset, one that’s curious, adaptive, and ready to explore what’s possible. We welcome thoughtful use of AI to expand your perspective and elevate how you share your story, while ensuring your application remains rooted in your own background, judgment, and voice.
About Us
CDW is a Fortune 500 technology solutions provider that helps businesses, government, education, and healthcare organizations achieve what’s possible through technology. What makes CDW different isn’t just what we do—it’s how we do it. At CDW we act as one—building trust, speaking candidly, and working together to achieve more. We play to win—focusing on what matters most and delivering for our customers. And we think forward—staying curious, moving fast, and continuously learning. We believe meaningful work happens when people feel supported, heard, and empowered to contribute. That’s why we think of ourselves as coworkers, not just employees—working together to solve complex challenges and deliver real impact for our customers and communities.
As a full‑stack, full‑lifecycle technology partner, CDW brings deep expertise, strong relationships, and broad industry knowledge to help turn ideas into outcomes. When you join CDW, you become part of a collaborative environment where your work matters, your growth is supported, and your contributions help shape what’s next.
Together, we deliver the full promise of what technology can do. Together, we Make Amazing Happen.
CDW is an equal opportunity employer. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status or any other basis prohibited by state and local law.
