Sales Engineering Leader, Enterprise Analytics & AI

Description

This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.

"VAST's data management vision is the future of the market." - Forbes

VAST Data is the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, VAST takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.

Our success has been built through intense innovation, a customer-first mentality and a team of fearless VASTronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our company’s growth and at a pivotal point in computing history.

We are seeking an experienced and strategic Sales Engineering Leader to drive our Enterprise Analytics and AI sales initiatives. This individual will lead a high-performing team of sales engineers, providing technical expertise, solution consulting, and pre-sales support to enterprise customers. The ideal candidate will have deep experience in data analytics, AI/ML, and enterprise software sales, with a passion for solving complex business challenges through innovative technology solutions.

Key Responsibilities:

• Lead and mentor a team of Sales Engineers, fostering technical excellence and customer-centric engagement.
• Develop and execute sales engineering strategies to support the company’s Enterprise Analytics and AI growth objectives.
• Partner closely with Sales, Product, and Customer Success teams to align technical solutions with customer needs.
• Drive technical pre-sales activities, including product demonstrations, proof-of-concepts (PoCs), and solution architecture discussions.
• Establish best practices for customer engagements, ensuring technical validation and alignment with business outcomes.
• Collaborate with marketing and product teams to create compelling technical content, case studies, and sales enablement materials.
• Stay abreast of industry trends, competitive landscape, and emerging technologies in analytics, AI, and enterprise software.
• Influence product roadmap and innovation by gathering feedback from customer engagements and market trends.
• Own key customer relationships, serving as a trusted advisor in analytics and AI-driven digital transformation initiatives.
• Drive revenue growth by supporting complex sales cycles and ensuring the technical win in enterprise deals.

Requirements

• 10+ years of experience in sales engineering, solution architecture, or technical pre-sales roles in the analytics, AI, or enterprise software space.
• 5+ years of leadership experience managing and scaling high-performing technical pre-sales teams.
• Strong understanding of enterprise data platforms, data warehouses, data lakes, and analytics architectures.
• Hands-on expertise in query engines such as Spark, Trino, vector databases, and retrieval-augmented generation (RAG) is required.
• Proven ability to drive technical engagements with C-level executives and business stakeholders.
• Excellent communication and presentation skills, with the ability to articulate complex technical concepts to non-technical audiences.
• Experience working with enterprise sales teams in complex, consultative sales cycles.
• Bachelor's degree in Computer Science, Engineering, or a related field; MBA or equivalent experience preferred.

Preferred Qualifications:

• Experience with AI/ML frameworks, data engineering, and big data technologies.
• Knowledge of regulatory and compliance considerations in AI and data analytics solutions.
• Hands-on experience with data science tools, visualization platforms, and model deployment workflows.