Sales Engineer

About Shelf:

There is no AI Strategy without a Data Strategy. Getting GenAI to work is mission critical for most companies but 90% of AI projects haven't deployed. Why? Poor data quality - it is the #1 obstacle companies have in getting GenAI projects into production.

We've helped some of the best brands like Amazon, Mayo Clinic, AmFam, and Nespresso solve their data issues and deploy their AI strategy with Day 1 ROI.

Simply put, Shelf unlocks AI readiness. We provide the core infrastructure that enables GenAI to be deployed  help companies deliver more accurate GenAI answers by eliminating bad data in documents and files before they go into an LLM and create bad answers.

Shelf is partnered with Microsoft, Salesforce, Snowflake, Databricks, OpenAI, and other big tech players who are bringing GenAI to the enterprise.

Our mission is to empower humanity with better answers everywhere.

The Sales Engineer will serve as a forward-deployed technical expert, independently leading proof-of-value engagements with enterprise prospects. This role combines deep technical expertise in data modeling and ontology design with hands-on implementation skills to deliver rapid-cycle pilots that demonstrate Shelf's value and convert to closed deals.

This is a unique opportunity to work at the cutting edge of AI and data quality, designing custom data models and configuring reasoning agents to solve complex enterprise challenges. You'll operate with significant autonomy, embedded with prospects during 3-5 day pilots, iterating quickly to prove measurable value. Your ability to understand customer data architectures, design semantic models, and deliver technical solutions will be crucial to our sales success.

Reporting to the Field CTO, you'll partner with Account Executives throughout the sales cycle, with primary responsibility during technical evaluation and POV phases. Beyond new customer acquisition, you'll support expansion opportunities within existing accounts, contributing to our land-and-expand growth strategy. You'll work directly alongside Engineering during POVs and collaborate on building reusable templates and best practices.

The ideal candidate brings 7-10+ years of experience combining technical depth in data platforms and modeling with customer-facing skills. As a self-starter, you'll quickly ramp through hands-on internal projects that mirror customer engagements. You're comfortable writing scripts, leveraging AI coding tools like Claude Code, and delivering approximately 8-10 POVs per quarter. Experience with knowledge graphs, ontologies, or semantic technologies is strongly preferred, though exceptional data modeling expertise can substitute.

• Technical Depth - Strong data modeling, data platforms, and ETL/pipeline expertise; knowledge graphs and ontologies experience strongly preferred
• Forward-Deployed Engineer - Comfortable operating independently with prospects, leading technical engagements with minimal supervision
• Technical Problem Solver - Writes Python scripts, automates workflows, and leverages AI coding tools to rapidly build and iterate solutions during customer engagements
• Customer-Facing Excellence - Exceptional communication skills with technical and business stakeholders; comfortable presenting and iterating with customers
• Rapid Execution - Thrives in fast-paced 3-5 day pilot cycles; delivers value quickly and iterates based on feedback
• Sales-Oriented Mindset - Understands enterprise sales cycles and is motivated by converting POVs to closed deals
• Continuous Learner - Stays current with AI/GenAI trends, data technologies, and modern engineering practices

• Lead independent POV engagements with enterprise prospects, designing custom ontologies and configuring solutions within 3-5 day cycles
• Conduct technical discovery and present solutions to Data Engineers, Enterprise Architects, and AI/ML Engineers
• Support Account Executives throughout sales cycles with primary ownership during technical evaluation and POV phases
• Work directly with Engineering on technical challenges and custom implementations during POVs
• Continue engagement through deal closing and initial customer onboarding alongside Customer Success
• Support existing customer expansion opportunities as part of land-and-expand strategy
• Collaborate with Field CTO to build reusable ontology templates and POV best practices

Qualifications:

• 7-10+ years in technical pre-sales, data engineering, solutions architecture, or similar customer-facing technical roles
• Strong expertise in data modeling, data platforms, and ETL/data pipelines
• Experience with knowledge graphs, ontologies, or semantic technologies strongly preferred
• Proficiency in scripting and automation (Python preferred)
• Hands-on experience with AI coding tools (Claude Code, Cursor, Git Hub Copilot, etc.)
• Deep understanding of enterprise data platforms and cloud architectures (AWS, Azure, GCP)
• Proven track record in customer-facing technical roles with…