Senior Fullstack Engineer, Customer Context

CanadaRemoteFull-timesenior

EngineeringFullstack Developer

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Fullstack Engineer, Customer Context based in Canada.

Technical Tools

EngineeringFullstack Developer

As a Senior Fullstack Engineer, you’ll help build the foundational services, APIs, and data pipelines powering an AI-driven e-commerce messaging platform. You’ll combine strong backend engineering with expertise in data-intensive systems, performance, scalability, and reliability. The role offers significant technical ownership, with responsibility for designing and evolving distributed systems across both new and established product areas. You’ll work closely with software engineers, data engineers, product teams, and go-to-market partners to turn customer context and data into better targeting and AI-powered experiences. AI-first development is central to the engineering culture, with an expectation that you actively use agentic coding tools and explore emerging techniques. You’ll have the autonomy to make technical decisions, solve ambiguous problems, and improve how engineering teams build and operate software. This is a fully remote opportunity suited to a pragmatic, curious senior engineer who enjoys high-impact work in a fast-moving environment.

Take deep ownership of key systems and product areas, becoming the technical expert and primary point of reference for the areas you own.
Independently investigate, design, and deliver solutions across complex distributed systems, particularly where requirements and implementation paths are ambiguous.
Architect, build, and maintain highly available and scalable REST APIs and backend services for internal and external applications.
Drive system decoupling, modularization, and technical debt reduction while balancing long-term maintainability with near-term delivery needs.
Use agentic development workflows as a standard part of engineering practice, leveraging autonomous and concurrent AI coding agents while taking full responsibility for the resulting code.
Stay current with emerging AI models, coding tools, agentic workflows, and engineering practices, and share valuable techniques with the broader team.
Help shape how AI is incorporated into engineering systems and workflows, identifying opportunities to create meaningful leverage across the organization.
Collaborate proactively with Product, Data, and go-to-market teams within an async-first environment.
Identify and eliminate single points of failure while improving system resilience, operational knowledge, and team redundancy.
Improve engineering productivity by introducing better tools, removing workflow friction, and developing internal utilities where automation can create compounding leverage.

Requirements

~2 min read

6+ years of experience building large-scale, data-intensive backend systems and APIs.
Demonstrated daily use of AI coding tools such as Claude Code, Codex, Cursor, Copilot, or equivalent, with concrete examples of how these tools have improved your engineering workflow and output.
Strong experience with one or more relevant languages, including Python, Go, Rust, React, and TypeScript.
Active interest in the AI tooling ecosystem, including new model releases, agentic development workflows, and emerging engineering best practices.
Strong judgment around AI-generated code, including understanding where AI accelerates development, where it introduces risk, and how to effectively validate its output.
Ability to switch efficiently between multiple concurrent workstreams while maintaining quality and delivery.
Experience working on both 0-to-1 initiatives and complex existing systems, with sound judgment around when to build, refactor, or extend.
Working knowledge of event-driven distributed systems, Kafka, and distributed data-processing technologies such as Flink or Spark.
Experience scaling production systems under significant customer load, ideally from early-stage infrastructure to multi-terabyte scale.
Strong AWS experience and familiarity with a broad range of database technologies, including relational databases, OLAP systems, and NoSQL technologies.
Strong relational data modeling and database indexing skills, with the ability to write performant SQL for both transactional and analytical workloads.
Experience with infrastructure as code, such as Terraform, and confidence evaluating and implementing infrastructure changes.
Familiarity with AWS and Kubernetes application deployment and observability, ideally using tools such as Datadog, SigNoz, or comparable platforms.
Clear, direct, and proactive communication skills, particularly in cross-functional and asynchronous environments.
Strong technical judgment and the ability to balance engineering best practices with business priorities and delivery requirements.