GTM Engineer

7shifts•Toronto, ON

50d•$92,000 - $130,000•Hybrid

About The Position

As the GTM Engineer at 7shifts, you’ll build the technical engine behind our global GTM strategy, transitioning our motions from manual one-size-fits-all campaigns to an intelligent system that triggers the right action at the right moment. Reporting to the Senior Manager, GTM Data Science & Engineering, your focus will be on building internal products and automated workflows that drive results across acquisition, retention, and expansion. As a strategic technical partner to GTM teams, you will engineer seamless experiences that turn complex data into business value at scale.

Requirements

2+ years of experience in GTM Analytics, Growth Engineering, Revenue Operations, or a similar role. You understand the technical side of acquisition, retention, and expansion.
Advanced proficiency in SQL and Python or JavaScript. You are comfortable building lightweight internal tools, custom scripts, and managing complex data transformations.
Hands-on experience with cloud data warehouses (e.g., Snowflake, BigQuery) and dbt for managing business logic and ensuring data quality.
Deep understanding of APIs and the ability to architect data flows between disparate GTM tools (Salesforce, Braze, etc).
The ability to act as a strategic bridge, translating high-level business goals into technical specs while helping non-technical stakeholders operationalize data signals.
A business-first engineering mindset. You think end-to-end from raw data extraction to activation, focusing on revenue impact rather than just shipping code.
Experience integrating LLMs into production environments for tasks like unstructured data extraction, lead qualification, or building autonomous agentic workflows.

Nice To Haves

Experience working with Snowflake or Snowflake data
Knowledge of restaurant operations/sector
Product-led Growth (PLG) experience

Responsibilities

GTM Tool Building: Build internal products that replace manual research with automated intelligence, enabling teams to shift from generic GTM motions to personalized, data-backed approaches.
Data Acquisition & Signal Engineering: Engineer workflows that blend vendor data with custom signals to identify high-value prospects based on specific triggers and market events across GTM initiatives.
Targeting & Prioritization: Replace manual list-building with data-driven prioritization models, ensuring resources are focused on the prospects and growth opportunities with the highest probability of conversion across the funnel.
AI Agents & Workflows: Deploy AI agents to automate routine tasks, increasing organizational capacity and enabling teams to maintain high-quality decision making across workflows.
Lifecycle Orchestration: Orchestrate cross-functional GTM motions using event-driven triggers, ensuring that interactions are synchronized with real-time behaviour to improve engagement timing and relevance.
Data Pipelines & Activation: Establish a centralized GTM logic layer within the data warehouse to ensure all execution platforms operate from a unified source of truth, maintaining data integrity across initiatives.
Cross-Functional Enablement: Serve as a strategic bridge between GTM and Data teams, translating business goals into technical requirements and enabling stakeholders to effectively apply data to optimize their core motions.