Principal Engineer – QA Automation

Role Overview

• As Principal Engineer – QA and Automation you will be focused on:
• Designing and building automated verification systems that validate AI-generated code at scale — ensuring agent output meets correctness, integration, and compliance standards before it ever reaches a human reviewer.
• Architecting oracle-based testing strategies — capturing reference behaviour from existing systems and using it to verify that new implementations produce equivalent results. You’ll build the infrastructure that makes behavioural equivalence testing continuous and automated.
• Defining risk-calibrated verification strategies across the platform — payroll calculations and legislative compliance demand near-zero-defect rigour, while UI and infrastructure changes can tolerate lighter-touch approaches. You decide what level of scrutiny each change type gets.
• Building cross-service integration testing infrastructure that catches failures across domain boundaries. When AI agents generate code that compiles and passes unit tests but breaks at the seams, your systems catch it.
• Designing and maintaining comprehensive scenario corpora derived from real customer data, legislative requirements, and edge cases — the living test suites that define “correct” for every release.
• Building nightly regression harnesses that run the full scenario corpus, automatically triage failures, and surface actionable diagnostics.
• Defining the quality gates in our delivery pipeline — no code (human or AI-generated) reaches production without passing through your verification layers. You own the “fit to ship” decision framework.
• Establishing property-based and golden-master testing for deterministic domains — payroll calculations that must always produce the same output for the same input, tax tables that must match published rates, leave entitlements that must comply with legislation.
• Building the test infrastructure that enables continuous delivery — test data generators, environment provisioning, parallel execution, and reporting that scales with the pace of AI-assisted development.
• Mentoring engineers on verification-first thinking — shifting the team’s mindset from “testing after the fact” to “defining correctness before generation begins.”

Requirements

• Deep experience designing test automation at scale — framework architecture, not just test writing.
• Strong understanding of verification strategies beyond unit testing — behavioural equivalence, contract testing, golden-master testing, integration testing across distributed systems.
• Experience embedding quality gates into CI/CD pipelines — you know how to make the build pipeline enforce correctness without becoming a bottleneck.
• Proficiency in at least one of: C#/.NET, TypeScript/Node.js, or Python for building test tooling and automation frameworks.
• Experience testing event-driven or event-sourced systems — you understand the challenges of verifying systems where state is derived from streams of events rather than direct database queries.
• Strong analytical skills — you can look at a complex, regulated domain (payroll, compliance, financial calculations) and decompose it into a set of verifiable properties and scenarios.
• Experience with end-to-end testing frameworks (Playwright or similar) and the infrastructure to run them reliably at scale.
• Track record of building test data management solutions — scenario corpora, synthetic data generation, environment seeding — that enable comprehensive regression testing.
• Understanding of software quality beyond code coverage metrics — you think in terms of correctness, equivalence, confidence, and risk.
• Excellent communication skills — you can articulate quality risk to engineering leadership and translate compliance requirements into testable acceptance criteria.
• Nice to have: Experience validating the outputs of AI or autonomous systems — you understand how to build confidence in non-deterministic code generation and have thought about what “correct” means when agents write code.
• Background in regulated domains (payroll, finance, healthcare) where calculation correctness and legislative compliance are non-negotiable.
• Experience with property-based testing, mutation testing, fuzzing, or formal verification techniques.
• Familiarity with observability-driven quality — using distributed tracing, structured logging, and production metrics to complement pre-deployment testing.
• Experience defining quality strategies for platforms undergoing a major technology transformation or replatforming effort.

Tech Stack

• Distributed Systems
• JavaScript
• Node.js
• Oracle
• Python
• TypeScript
• .NET