World Wide Technology
Technical Solutions Architect (Data Engineerning)
London
Why WWT?
World Wide Technology (WWT) strives to make a new world happen. WWT's work benefits clients and partners as much as it does its people and community across the globe.
World Wide Technology
Technical Solutions Architect (Data Engineerning)
London
Why WWT?
World Wide Technology (WWT) strives to make a new world happen. WWT's work benefits clients and partners as much as it does its people and community across the globe. Founded in 1990, WWT brings together strategy, deep technical expertise and world‑class partnerships to help public and private sector organizations design, build and scale intelligent AI, digital, cybersecurity, cloud and infrastructure solutions. Through its Advanced Technology Center (ATC)—a collaborative ecosystem featuring state‑of‑the‑art hardware and software—WWT enables clients and partners to conceptualize, test and validate innovative technology and then deploy solutions at scale using its global integration and distribution capabilities. With more than 14,000 team members and over 60 locations globally, WWT's culture—grounded in core values and leadership philosophies—has been recognized by Fortune® and Great Place to Work for its commitment to innovation, trust and creating a great place to work for all. WWT provides products and services to large enterprise, global service provider and public sector clients in up to 130 countries across six continents. Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.S. and Canada. Want to work with highly motivated individuals on high-performance teams? Join WWT today!
What will you be doing?
The AI & Data Solutions team operates as a pre‑sales advisory practice within WWT’s GS&A organization, helping organizations move from AI interest to AI impact. This role is grounded in data engineering, with ideal candidates bringing deep hands‑on data platform expertise into customer conversations and translating that technical dept into business clarity and confident decision‑making. This role makes the engineering path from data foundation to AI impact concrete and actionable for customers. The role will partner with account teams across the full sales cycle, converting that clarity into services opportunities for WWT.
Responsibilities
- Pre‑Sales Engagement: Independently lead pre‑sales enterprise customer engagements, including workshops, discovery sessions, architecture reviews, and executive briefings, focusing on data readiness for AI and the practical path from data foundation to AI value.
- Opportunity Support: Advance opportunities across the AI Studio, AI Foundry, and AI Factory offerings, with particular emphasis on data strategy, data engineering maturity, and AI‑ready data architecture.
- Business Translation: Translate complex technical concepts into language that resonates with business and executive stakeholders by connecting technology directly to outcomes.
- Thought Leadership: Author and contribute technical content such as whitepapers, workshop curriculum and internal enablement that document field‑tested approaches for AI‑ready data
- Partner Ecosystem Engagement: Engage with WWT’s AI Proving Ground and partner ecosystems, particularly Databricks and Snowflake, and similar technologies to develop insights, validate approaches, and support field enablement.
- Work Experience: 10+ years of experience designing, building, and optimizing scalable data platforms, with strength in Snowflake, Databricks and modern Lakehouse architecture. Prior experience in a pre‑sales, consulting, solutions engineering, or technical advisory capacity within an enterprise technology organization is preferred.
- Deep hands‑on experience with modern cloud data platforms, particularly Snowflake and Databricks. This includes platform capabilities such as Snowflake's Snowpark, Dynamic Tables, Streams & Tasks, and Snowflake Cortex, as well as Databricks components such as Lakeflow (Connect, Pipelines, and Jobs), Delta Lake, and Unity Catalog.
- Strong data engineering fundamentals: ETL/ELT pipeline design and implementation, data orchestration and workflow automation, batch and streaming processing, and data modeling for analytical and operational workloads.
- Proficiency in SQL and Python sufficient to write, debug, and review production‑quality code independently.
- Working fluency in lakehouse and data platform architecture — able to reason through platform tradeoffs and answer architecture‑level questions in real time alongside engineering questions, since customers routinely expect both in the same conversation.
- Governance fluency: able to represent data quality, security and trust topics credibly in customer conversations, while governance strategy and roadmap ownership sit with a dedicated specialist role.
- Practical understanding of how AI workloads — LLMs, RAG, agentic AI — consume enterprise data. The emphasis is on engineering trusted, scalable data foundations, not building AI models.
- Experience integrating and using AI coding assistants and agent tools (e.g., Claude, Copilot, Glean, Snowflake Cortex Code) with cloud data platforms.
- Experience implementing Databricks and Snowflake solutions on Azure, AWS, or Google Cloud.
- Advisory mindset and the ability to lead customers through ambiguous technical challenges: structuring discovery engagements, identifying technical and organizational gaps, evaluating platform tradeoffs objectively, and delivering actionable recommendations.
- Experience supporting a services sales motion in a non‑quota‑carrying, technical advisory capacity — partnering with account teams to shape and advance service engagements.
- Strong communication skills across audiences — data engineers, architects, IT leadership, and executive stakeholders — tailoring technical depth while maintaining credibility with each.
- Experience with scoping and/or delivering large‑scale data platform migrations.
Preferred
- Experience with additional cloud data platforms such as Google BigQuery, AWS Redshift, or Azure Synapse.
- CI/CD, DevOps, and Infrastructure as Code practices for data platforms.
- Metadata management, lineage tooling, and data observability/monitoring experience.
- Familiarity with dbt, Apache Airflow, Azure Data Factory, Kafka, Event Hubs, or comparable orchestration/integration tools.
- Familiarity with enterprise AI platforms — Azure AI, AWS SageMaker, Google Vertex AI, Databricks Mosaic AI, NVIDIA NIM, or similar.
- A passion for helping customers solve complex business problems through modern data engineering and trusted data foundations.
Education
Bachelor’s degree in computer science, data engineering, or a related field, or equivalent experience.
Certifications
Active Databricks and/or Snowflake certification(s) highly preferred.
WWT offers a challenging and rewarding position in a growing and truly international environment, a competitive salary, bonus opportunities, and benefits
Equal opportunities
We strive to create an environment where all employees are empowered to succeed based on their skill, performance, and dedication. Our goal is to cultivate a culture of belonging that encourages innovation, collaboration, and respect for all team members, ensuring that WWT remains a great place to work for all!