Technical Solutions Architect II -- Data Engineer

* 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 technica