Technical Solutions Architect II -- Data Engineer

World Wide Technology helps organizations design, build, and scale intelligent AI, digital, cybersecurity, cloud, and infrastructure solutions. The Technical Solutions Architect II -- Data Engineer role leads pre-sales advisory engagements focused on data readiness for AI, modern data platforms, and lakehouse architecture, while partnering with account teams to develop services opportunities. The role also translates complex technical concepts for business stakeholders and contributes to technical thought leadership and partner enablement.

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

Requirements:

• 10+ years of experience designing, building, and optimizing scalable data platforms, with strength in Snowflake, Databricks and modern Lakehouse architecture
• 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
• Bachelor's degree in computer science, data engineering, or a related field, or equivalent experience
• Prior experience in a pre-sales, consulting, solutions engineering, or technical advisory capacity within an enterprise technology organization is 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
• Active Databricks and/or Snowflake certification(s) highly preferred