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NEXTMOVEFDE careers · United States
Planhat

Forward Deployed Engineer | Americas

AI summary of the role

Founding Forward Deployed Engineer at Planhat, embedded with customers to deploy AI solutions that drive measurable business outcomes.

What you’ll do

  • Embed with customers to understand their business and systems, and engineer data pipelines, workflows, and automations that make AI useful.
  • Deliver AI-powered outcomes for customers that are measurable, scalable, and repeatable.
  • Act as a trusted technical partner, bridging real-world complexity and AI systems.
  • Build the playbook for Forward Deployment Engineering at Planhat.

What you’ll bring

  • 3–6 years of experience in consulting, data engineering, software engineering, or similar technical roles.
  • Strong foundation in SQL and data modeling.
  • Experience with Python and/or other scripting languages.
  • Experience integrating with APIs (REST / GraphQL).

Technologies

SQL · data modeling · Python · REST · GraphQL · agentic systems · AI tools · data pipelines · ETL/ELT · workflow automation

Source and classification

Internal deployment & tooling · Evidence for this classification:

pipelines, ETL/ELT, or workflow automation is a bonus but not required Trajectory Forward Deployed Engineers are the heart of Planhat's AI Deployment Program. Joining Planhat as a Founding FDE at this moment is a unique professional accelerant: Work with companies that are early adopters of AI and want to build the future with us Gain experience in data engineering, systems design, AI deployment, workflow automation, and commercial operations — all at once Become fluent in applied AI: not model research, but the much harder, much rarer skill of deploying AI that actually works Develop technical intuition that is extremely rare in the market — the type that opens doors to engineering leadership, product architecture, or applied AI roles in any industry Shape a brand-new discipline. FDE is to this decade what full-stack engineering was to the previous one As our Founding FDE,
More from the job description

Competencies This isn't traditional consulting. And it's not back-office engineering. It's applied problem-solving in the real world, where your decisions directly impact revenue, retention, and business outcomes. We're looking for someone with: 3–6 years of experience in consulting, data engineering, software engineering, or similar technical roles Comfort working directly with customers and operating in ambiguous environments Curiosity, ownership, and interest in building something new Willingness to travel to customer sites The ability to learn fast — this is the most important trait of all Technical foundations: Strong foundation in SQL and data modeling Experience with Python and/or other scripting languages Experience integrating with APIs (REST / GraphQL) Strong foundation in agentic systems and experience with AI tools to accelerate delivery Familiarity with data pipelines, ETL/ELT, or workflow automation is a bonus but not required Trajectory Forward Deployed Engineers are the heart of Planhat's AI Deployment Program. Joining Planhat as a Founding FDE at this moment is a unique professional accelerant: Work with companies that are early adopters of AI and want to build the future with us Gain experience in data engineering, systems design, AI deployment, workflow automation, and commercial operations — all at once Become fluent in applied AI: not model [... source excerpt omitted ...] ct architecture, or applied AI roles in any industry Shape a brand-new discipline. FDE is to this decade what full-stack engineering was to the previous one As our Founding FDE, you'll embed with customers, understand how their business and systems actually work, and engineer the data pipelines, workflows, and automations that make AI useful and valuable. Definition of Success You are continuously delivering AI-powered outcomes for customers that are measurable, scalable, and repeatable You are consistently seen as a trusted technical partner by customers, bridging the gap between messy real-world complexity and AI systems that actually work You are building the playbook

How jobs are selected

Employer postings · Data from · Sources