Software Engineer at Cannabix Technologies Inc
Burnaby, BC V5A 4R4, Canada -
Full Time


Start Date

Immediate

Expiry Date

26 Nov, 25

Salary

75000.0

Posted On

26 Aug, 25

Experience

3 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Software Development, Python, Git, Production Systems, Computer Science

Industry

Computer Software/Engineering

Description

We’re looking for a software and data engineer who is experienced in building product-level, scalable systems that integrate seamlessly from device to cloud. This role is ideal for someone who has worked on real-world products in mass production and is fluent in developing full-stack applications, cloud-based dashboards, and database infrastructures to support high-volume sensor data.
You’ll help us collect, process, analyze, and visualize data, transforming technical complexity into real-time insights and automated tools.

REQUIRED QUALIFICATIONS

  • Degree in Computer Science, Engineering, or a related discipline
  • 3–7 years of experience in product-level software development
  • GUI (graphic user interface) development experience
  • Experience building and maintaining AWS-based data platforms
  • Proficient in full-stack tools (e.g., Python, JS, etc.)
  • Strong troubleshooting and debugging skills
  • Familiarity with DevOps tools like Git
  • Experience deploying and supporting mass production systems

How To Apply:

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Responsibilities
  • Build and maintain full-stack applications for real-world products (Python, JavaScript (preferred), or any other language such as React, etc.)
  • Design, deploy, and manage cloud-based infrastructure on AWS (S3, DynamoDB, Lambda, CDN)
  • Architect non-relational databases to store large-scale device data
  • Develop internal dashboards and tools for reporting, analytics, and insights
  • Implement data pipelines to automate ingestion, validation, storage, and reporting
  • Support DevOps workflows including version control
  • Collaborate with cross-functional teams to ensure software reliability in production
  • Occasionally support experimental setups, calibration workflows, or testing routines
  • Machine Learning experience
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