Full Description

About the Company

Enerva designs and delivers energy efficiency and decarbonization programs on behalf of utilities and government agencies across Canada. We help homes, businesses, and industrial facilities cut their energy use, costs, and emissions — and we build the software that makes those programs run. Founded in 2017, we're 100% Canadian and employee-owned, with 60+ people across Toronto and Calgary and $1B+ in program funding under management. We're ISO/IEC 27001 certified. Our software team is small and deliberately so. You'd be joining a compact group inside an established company: the autonomy and visibility of an early-stage team, without the instability that usually comes with it. The decisions you make stick.

About the Role

We're looking for a QA Engineer to own quality across the software we build — the web applications, data tools, and AI features behind our energy and emissions programs. You'd be our first dedicated QA hire. You're not inheriting a test plan. You're writing one. Much of what we build produces numbers that end up in front of utilities, government agencies, and program participants: energy savings, emissions reductions, incentive amounts. A bug that makes a page look wrong is annoying. A bug that makes a number wrong is a much bigger problem. Holding that line is the job. This is a hands-on role for someone systematic, skeptical, and happy to build their own tooling when the off-the-shelf option doesn't fit.

Responsibilities

Verify the math. Trace energy, emissions, and incentive calculations back to source data and confirm the output is right, not just plausible. Test our web applications and data tools before they reach our team and our clients — exploratory, functional, and regression testing across releases. Help us figure out how to test AI and LLM features, where the output isn't identical twice and "correct" has to be defined before it can be measured. This is genuinely unsolved territory and we're looking for someone who finds that interesting rather than alarming. Write and maintain automated tests so the same bug doesn't ship twice — starting where automation pays off most and building out from there. Test APIs and integrations, including how our systems behave when upstream data arrives late, malformed, or not at all.

File bugs developers can act on: clear reproduction steps, expected versus actual, and an honest read on severity. Own pre-release checks and support user acceptance testing with our program delivery team, translating what users report into what's actually broken. Flag requirements that are ambiguous or untestable before they get built, not after.

What Success Looks Like

First 90 days — You know the systems and where the risk concentrates. There's a documented approach to verifying calculations, and automated coverage on the paths where a defect would do the most damage. First 6 months — Releases go out against a defined process rather than an ad hoc one. Regression coverage is real. The team has a shared, written definition of what "correct" means for our AI features. First year — QA is a standard part of how we build, not a stage bolted on at the end. You've set a bar the next hire inherits.

Qualifications

2+ years testing software, or equivalent hands-on capability you can demonstrate. New grads are welcome — see the note on our process below.

Required Skills

A tester's instinct. You find the input nobody considered and the workflow nobody documented. Enough scripting in Python or JavaScript/TypeScript to automate a test, read the code you're testing, and build small tools when you need them. Comfort with SQL, and the habit of validating data at the source rather than trusting what the UI shows you. Strong written communication. Most of your output is a written case that something is broken and why it matters. A self-starter streak. You'll be defining the process here, not following one.

Preferred Skills

Test automation frameworks (Playwright, Cypress, Selenium, pytest) and running suites in CI, e.g. GitHub Actions. API and integration testing tools (Postman or similar), and some exposure to performance or load testing. Experience testing AI/ML or LLM-based features, including building evaluation sets. Accessibility testing (WCAG / AODA). Some of our client contracts require it — if you haven't done it before, we'll support you in getting up to speed. Familiarity with cloud environments (AWS or Azure) and deployment pipelines. Interest or background in energy, sustainability, or climate.