Solution Engineer - Insurance & Asset Management

About the role

You will own the end-to-end solution architecture for insurance and asset management customers as they migrate to the AI Data Cloud. You will translate complex business requirements into compelling, value-based Snowflake demonstrations that directly influence deal progression. You will act as the primary technical partner for enterprise Proofs of Concept, ensuring successful implementation and measurable outcomes. You will collaborate closely with sales teams and channel partners to design winning sales cycles that align with customer decision-making processes. You will immerse yourself in the evolving insurance and asset management landscape to identify competitive advantages and relevant use cases. You will leverage your expertise in Generative AI to articulate how the AI Data Cloud drives tangible business value for risk, claims, and portfolio management. You will continuously refine your technical knowledge of data platforms to position Snowflake as the strategic foundation for digital transformation in this sector.

Key facts

What you'll do

Present Snowflake technology and vision, focusing on the AI Data Cloud and its capabilities, to executives and technical contributors at prospects and customers in the insurance and asset management sectors.

Work hands-on with prospects and customers to demonstrate and communicate the value of Snowflake technology throughout the sales cycle, from initial demo to enterprise Proof of Concept design and implementation.

Immerse yourself in the ever-evolving insurance and asset management industry, analyzing competitive and complementary technologies and vendors to clearly position Snowflake against them.

Collaborate with Product Management, Engineering, and Marketing to refine Snowflake's products and messaging based on real-world customer feedback from the insurance and asset management vertical.

Translate complex business requirements into technical solution designs that leverage Snowflake's core data concepts, data modeling, and data governance best practices.

Provide compelling value-based demonstrations that highlight Generative AI and Machine Learning use cases, showcasing their impact on enterprise decision-making and operational efficiency.

Support the creation and execution of enterprise Proofs of Concept, guiding technical discussions and ensuring successful outcomes that accelerate deal closure.

Leverage hands-on expertise with Python and SQL to build and validate data pipelines, integrations, and analytics within the Snowflake AI Data Cloud environment.

Maintain a deep understanding of modern AI/LLM ecosystems, including Retrieval-Augmented Generation (RAG), and utilize frameworks such as LangChain, Hugging Face, or PyTorch to deploy Large Language Models effectively.

Act as a trusted technical advisor to sales teams and channel partners, bridging the gap between executive strategy and technical implementation details.

Requirements

You must possess outstanding presenting skills and executive presence to engage technical, business, and executive audiences through both impromptu discussions and structured presentations.

You must have a broad range of experience within data platforms, analytics, and cloud technologies, including deep knowledge of data warehousing, data lakes, and lake house architectures.

You must demonstrate a proven ability to discuss and demonstrate the value of Generative AI and Machine Learning use cases for enterprise stakeholders in regulated industries.

You must have hands-on experience with modern AI/LLM ecosystems, including Retrieval-Augmented Generation (RAG), and familiarity with open-source frameworks such as LangChain, Hugging Face, or PyTorch.

You must possess a solid understanding of how to work with and deploy Large Language Models (LLMs) in production environments while adhering to industry best practices.

You must have familiarity with core data concepts such as data modeling, data governance, and data engineering best practices specific to insurance and asset management data sets.

You must exhibit hands-on expertise with Python and SQL to develop, test, and optimize complex queries and data transformations.

You must be comfortable operating in dynamic and fast-moving environments, where flexibility and an experimental mindset are essential for navigating ambiguous requirements.

Practical notes

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