Senior Fullstack Engineer, Customer Context

New

JobgetherE-commerce messaging

Fully remote work from Canada or the United States.Full-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: AWSPythonKafkaTypeScriptGoReactRustSparkRESTful APIs

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, or Copilot, with concrete examples of workflow improvements.
Bring strong experience with one or more of Python, Go, Rust, React, and TypeScript.
Use sound judgment to validate AI-generated code and understand its risks and benefits.
Have experience with both 0-to-1 initiatives and complex existing systems.
Have working knowledge of event-driven distributed systems, Kafka, and data-processing technologies such as Flink or Spark.
Have experience scaling production systems under significant customer load, ideally to multi-terabyte scale.
Bring strong AWS experience and familiarity with relational databases, OLAP systems, and NoSQL technologies.
Have strong relational data modeling, database indexing, and SQL skills for transactional and analytical workloads.
Have experience with infrastructure as code, such as Terraform.
Be familiar with AWS and Kubernetes deployment and observability, ideally using Datadog, SigNoz, or comparable platforms.
Communicate clearly and proactively in cross-functional and asynchronous environments.

Take ownership of key systems and product areas, serving as the technical expert for areas owned.
Investigate, design, and deliver solutions across complex distributed systems with ambiguous requirements.
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.
Share useful AI models, coding tools, workflows, and engineering practices with the broader team.
Collaborate with Product, Data, and go-to-market teams in an async-first environment.
Improve system resilience by identifying and eliminating single points of failure.
Improve engineering productivity through better tools, reduced workflow friction, and internal utilities.

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