Senior Fullstack Engineer, Customer Context

JobgetherE-commerce messaging

CanadaFull-TimeSenior

SalaryBase salary of USD $175,000–$205,000, with no geographic salary adjustments.

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Job Details

Experience: 6+ years of experience building large-scale, data-intensive backend systems and APIs.
Required Skills: AWSPythonKafkaTypeScriptGoReactRustSparkTerraform

Have 6+ years of experience building large-scale, data-intensive backend systems and APIs.
Demonstrate daily use of AI coding tools such as Claude Code, Codex, Cursor, Copilot, or equivalent, with examples of their impact on your workflow and output.
Have strong experience with one or more of Python, Go, Rust, React, and TypeScript.
Show active interest in AI tooling, new model releases, agentic development workflows, and emerging engineering practices.
Apply sound judgment to AI-generated code and validate its output effectively.
Switch efficiently between concurrent workstreams while maintaining quality and delivery.
Have experience with both 0-to-1 initiatives and complex existing systems.
Understand event-driven distributed systems, Kafka, and distributed data-processing technologies such as Flink or Spark.
Have experience scaling production systems under significant customer load, ideally from early-stage infrastructure to multi-terabyte scale.
Bring strong AWS experience and familiarity with relational databases, OLAP systems, and NoSQL technologies.
Have relational data modeling, database indexing, and performant SQL skills for transactional and analytical workloads.
Have experience with infrastructure as code such as Terraform, and familiarity with AWS and Kubernetes deployment and observability tools such as Datadog or SigNoz.

Take ownership of key systems and product areas as the technical expert and primary point of reference.
Investigate, design, and deliver solutions across complex distributed systems and ambiguous problem areas.
Architect, build, and maintain highly available, scalable REST APIs and backend services.
Drive system decoupling, modularization, and technical debt reduction.
Use agentic development workflows and take responsibility for the resulting code.
Stay current with AI models, coding tools, agentic workflows, and engineering practices, and share useful techniques with the team.
Identify opportunities to incorporate AI into engineering systems and workflows.
Collaborate with Product, Data, and go-to-market teams in an async-first environment.
Improve system resilience by identifying single points of failure and strengthening operational knowledge and team redundancy.
Improve engineering productivity through better tools, workflow improvements, and internal utilities.

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