Senior Fullstack Engineer, Customer Context

PostscriptE-commerce messaging

This position is fully remote and can be based in the US or Canada.Full-TimeSenior

SalarySalary range of USD $175,000–$205,000 base plus significant equity

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Job Details

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

Have 6+ years of experience building large-scale data-intensive backend systems and APIs.
Use AI coding tools daily, such as Claude Code, Codex, Cursor, or Copilot, and be able to describe their effect on your output and workflow.
Have experience with some or all of Python, Go, Rust, React, and TypeScript.
Follow AI tooling developments, including model releases, agentic workflows, and evolving practices.
Critically assess AI output, understand its risks, and verify what it produces.
Context switch quickly across concurrent workstreams.
Have experience with both 0-to-1 initiatives and complex existing systems.
Have working knowledge of event-driven distributed systems and Kafka, and familiarity with distributed data processing technologies such as Flink or Spark.
Have experience scaling production systems under customer load, ideally to the multi-terabyte stage.
Have experience designing high-scale systems on AWS and working with RDBMS, OLAP, and NoSQL databases.
Model relational data, design index strategies, and write performant SQL for transactional and analytical workloads.
Have experience with infrastructure as code such as Terraform and be comfortable evaluating and applying infrastructure changes.
Be familiar with building and observing applications on AWS and Kubernetes, using Datadog, Signoz, or comparable observability tools.
Communicate clearly and proactively with cross-functional partners and async teams.

Take ownership of systems and product areas and become the technical expert for what you own.
Investigate, design, and execute solutions across distributed systems.
Architect, build, and maintain highly available, scalable REST APIs and backend services.
Decouple and modularize systems and reduce technical debt while maintaining delivery pace.
Use concurrent autonomous AI-agent workflows to build and manage aligned work.
Evaluate emerging AI tools and practices and share techniques with the team.
Help shape how AI is integrated into company systems and engineering workflows.
Collaborate with product, data, and go-to-market teams in an async-first environment.
Identify and eliminate single points of failure to improve resilience and knowledge redundancy.
Improve engineering workflows by introducing tools and building internal utilities.

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