Forward Deployed Engineer

Greenhouse•Ontario, ON

3d•CA$117,500 - CA$176,300•Remote

About The Position

As a Forward Deployed Engineer within our centralized Data Analytics function, you will sit at the intersection of Data Engineering, Analytics, and Generative AI. You won't just analyze data; you will build the AI-powered interfaces that allow our executives and stakeholders to interact with Greenhouse’s data foundation in real-time. You will be the architect of the "last mile," ensuring our core data products are not just stored in a warehouse, but are accessible, conversational, and actionable through modern AI orchestration. This role is primarily responsible for designing, developing, implementing, and operating AI-enabled information systems and data products.

Requirements

3-6 years of experience in a high-touch technical role such as forward deployed engineering, product analytics, analytics engineering, or analytics engineering supporting finance stakeholders
Documented success working directly with executive stakeholders to solve "messy" business problems through creative technical solutions
Deep understanding of data modeling within dbt or similar frameworks and a passion for structuring data for both clarity and performance
Proven ability to navigate the barrier between raw LLMs and structured business data with familiarity in RAG, function calling, or semantic modeling
Demonstrated ability to turn skeptical users into power users through thoughtful tool design, proactive education, and clear documentation
Experience with the modern data stack including version control (Git), orchestration tools like Airflow or Dagster, and cloud data warehouses such as Snowflake or BigQuery
Applicants must be legally eligible to work in Canada as of the start date chosen by the Company. We are unable to support sponsorship at this time.

Responsibilities

Build and maintain the "Last Mile" of our data stack, deploying AI-driven tools (via Streamlit, Retool, or internal frameworks) that allow stakeholders to query Greenhouse data in natural language
Establish "Human-in-the-loop" workflows where stakeholders (like Finance) can flag incorrect AI outputs. You will use these flags to refine context libraries, prompt templates, RAG retrieval logic, and dbt models
Create "AI Personas" tailored to different departments, ensuring users know how to get the most out of the system
Act as the "Translator-in-Chief" by enriching our data warehouse metadata so that AI models understand the nuances of Greenhouse’s business metrics
Build and own the "Golden Dataset", a collection of vetted business questions and their correct SQL answers, to rigorously test and grade AI performance