evertz

Senior Software Engineer, Cloud Backend

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At a Glance

Location
Burlington, Ontario, Canada
Experience
5+ years
Compensation
sting position. Salary Range: $100,000 to $135,000, based on relevant experience

Key Requirements

Required Skills

AIAWSAgileAngularAzureCI/CDComputer VisionData ScienceGCPJavaJenkinsMachine LearningPythonRustTerraformTypeScript

Certifications

  • SAFe

Domain Knowledge

  • Automation
  • Education
  • Engineering
  • Media
  • SaaS

Requirements

5+ years building and operating production backend or distributed systems (Python preferred; strong candidates from Java, Go, or similar backend languages also considered)

Deep, hands-on experience with a major cloud provider’s serverless and compute services (e.g., AWS Lambda, Step Functions, DynamoDB, EventBridge, EC2, or equivalent services on GCP/Azure)

Infrastructure-as-code fluency (CloudFormation, Terraform, or similar) and comfort owning deployments end-to-end

Solid understanding of event-driven architecture, distributed-systems failure modes, and idempotent/retry-safe design

Strong testing discipline and experience writing clean, maintainable, well-covered code

Track record of operating what you build - on-call, observability/monitoring, and incident response

Responsibilities

The evertz.io Engineering Team builds next-generation systems for content management and distribution in the Media and Entertainment industry.

Disney, NBCUniversal, Discovery, BBC, and many other content producers and publishers use our products and services to make the most of their file-based and live content for the least effort.

We work with high quality video in real-time and non-real-time scenarios across a wide range of cutting-edge tech.

Specializations within the group span from low-level video manipulation and analysis, through back-end management and orchestration services, to web delivered UIs.

There may also be opportunities for working as a member of the Scientific Computing Group who work in computer vision, data science and machine learning, taking experiments in Jupyter notebooks through to deployment in production.

Our technology stack includes a serverless microservice architecture that capitalizes on the full breadth of AWS services with code written in Python, Rust and Java.