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# Senior Software Engineer

- **Company:** Great American Restaurants, Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Information Engineering, Distributed Systems, Elasticsearch, PostgreSQL, Redis, TypeScript, Virtual Memory, ReactJS, Change Data Capture, Backend, Material UI, Kubernetes, Information Technology, Apache Kafka, Front End Software Development, Terraform, Stream Processing, Grpc, Golang
- **Published:** August 26, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pfe159vjf9

## About the Role

* Strong fundamentals in computer science
 * Pragmatic and care more about building the right thing vs. being right
 * Comfortable jumping around the stack and wearing different hats
 * Someone that wants to take an active role in defining the next gen data platform that utilizes stream processing and CDC to drive data latency to zero, while also focusing on ease-of-use and extensibility
 * Past experience working on scaling async systems and exposure to topics like gRPC, Kafka, Kubernetes, Helm is preferred but not required.
 * Proficiency writing in Go is preferred, but not necessary.

Tech stack

 * Frontend: TypeScript (React and Material UI)
 * Backend: Go, Postgres, Redis, Kafka and Elasticsearch
 * Infra: Terraform, Kubernetes, and Helm on GCP and AWS

## Description

We are a small team based in San Francisco looking to hire an engineer that has a dedicated focus around distributed systems. As a founding engineer, you will have the opportunity to wear multiple hats, directly interact with our customers and play a crucial role in shaping the future of our product.

This is a challenging and dynamic role and you will be working at the forefront of data engineering, distributed systems and infrastructure.

What you should expect & examples of day-to-day tasks:

 * Building column encryption, launching our analytics portal and schema change alerts.
 * Building tooling to streamline and automate infrastructure workloads.
 * Introducing pipeline efficiencies by making tradeoffs between virtual memory and disk.
 * Building native change data capture (CDC) connectors to data sources that we do not yet support.


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