Senior Forward Deployed Engineer, Snowflake

About the role

Deloitte seeks to recruit a Senior Snowflake FDE who can help clients transform AI ambitions into impactful enterprise-scale solutions. This role involves working closely with clients to develop and deliver AI solutions, collaborating with senior stakeholders, and contributing to the development of integrated sector solutions.

Responsibilities

• Embed with clients to understand business needs and translate these into AI solutions.
• Partner with leaders, product owners, architects, and engineers to align priorities and ensure successful delivery.
• Lead working sessions to shape solutions and drive client outcomes.
• Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
• Mentor newer team members and coach client teams and end users on platform capabilities and AI enablement.
• Drive end-to-end sales and delivery support by developing demos/POCs, contributing to proposals and orals, articulating business value, and documenting solutions for smooth client handoff and knowledge transfer.
• Contribute to reusable assets including code, prompt libraries, runbooks, and reference implementations.

Qualifications

• Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering.
• 5+ years of experience in software engineering, data engineering, data science, or analytics engineering.
• 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments.
• 1+ years of experience with Snowflake including hands-on experience with one of the following key platforms; Cortex AI, Cortex LLM Functions, Cortex Agents, Arctic Embed.
• 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions.
• 1+ years of experience building reliable, maintainable, and well-documented code.
• Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve.

Preferred Qualifications

• Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking).
• Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments.
• Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation.
• Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management.
• Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures.
• Familiarity with security, privacy, and compliance considerations.