Manager, Sales Engineering Specialists

Fivetran is building data pipelines to power the modern data stack for thousands of companies.

We’re looking for a Manager Sales Engineering Specialists to oversee our data platform Specialists functions. In this role, you will work alongside global Sales Engineering leadership to leverage a team of technical experts to accelerate our most complex enterprise deals, drive customer adoption after the sale, and improve the efficiency of the Sales Engineering motion at large. As customers build toward AI and agentic workloads, you will keep your team ahead of what those workloads demand of the data platform.

You will need a deep understanding of how modern data movement and transformation platforms like ours are architected, including the AI use cases built on top of them, and a keen ability to lead a team of highly specialized resources. You will be tasked with developing and retaining experienced, high-performing talent who can speak the language of enterprise architects, data engineers, and data platform owners, and with hiring where the team needs to grow.

This is a full-time position based in the US.

Fivetran, dbt, Enterprise databases and applications (including SAP), cloud data warehouses and lakehouses (Snowflake, BigQuery, Databricks), open table formats (Apache Iceberg), dbt, SQL, reverse ETL, AI and agent tooling, AWS, GCP, Azure

• →Lead a team of tenured experts across our data platform specialties to act as technical liaisons between Sales Engineering, Sales, Product, and Engineering.
• →Support Global Sales teams by proactively embedding specialist functions to remove technical blockers in complex enterprise deals and in customer adoption after the sale.
• →Implement strategies to maximize deal velocity and customer adoption by streamlining how specialists are engaged, prioritized, and measured.
• →Execute hands-on tasks when needed, including architecture reviews, critical customer calls, or complex proofs of value.
• →Ensure the team provides timely, strategic, and accurate guidance during technical evaluations and customer escalations.
• →Build strong working relationships with strategic internal partners, including Product, Engineering, and other sales teams.
• →Coach team members to maximize performance and efficiency, and prepare them for future roles.
• →Ensure the team shares specialist knowledge across the field through enablement and documentation.
• →Keep the team current on how AI and agentic workloads change customer requirements, and turn that into field guidance.

• People Leadership: Proven experience leading and developing a Sales Engineering, Solution Architecture, or other technical team, with people management experience preferred.
• Technical Depth: Hands-on expertise in one or more areas across the data lifecycle, such as database and application replication, SAP, lake and warehouse architectures, transformation, activation, or AI and context engineering, and the breadth to credibly lead specialists in other disciplines. Experience applying a narrower specialty across several of these areas also counts.
• Cloud and Data Architecture: Proficiency with major cloud providers (AWS, GCP, Azure), specifically regarding deployment and connectivity, and with modern data architectures, including cloud data warehouses and lakehouses.
• AI Fluency: Working understanding of how AI and agentic workloads change customers' data requirements, and fluency using AI tools to scale your own and your team's work.
• Technical Sales Acumen: Extensive Sales Engineering or specialist experience supporting complex enterprise sales cycles, including proofs of value and senior stakeholders, and deep knowledge of the broader technical sales and demonstration process.
• Communication Skills: Best-in-class ability to explain complex data and AI concepts to non-technical buyers and technical architects alike, and to influence Product and Engineering priorities with customer evidence.

• Prior experience at an analytics, ETL, BI, or open-source data software company
• Familiarity with dbt, Terraform, or similar tools for transformation and managing cloud infrastructure
• Experience managing distributed teams across multiple regions and time zones
• Hands-on experience with AI and context engineering, such as LLM applications, MCP, semantic layers, or metadata and retrieval