Head of Quality Engineering , Lendable

• Join us if you want to:
• Take ownership across a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1
• Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo
• Build the best technology in-house, using new data sources, machine learning and AI to make machines do the heavy lifting
• At least 7 years in quality related roles
• Run a number of QA initiatives across an organisation to improve the quality output across engineering teams
• Proven senior stakeholder management
• Experience working with quality across the stack. You have hands-on experience of testing backends and frontends. From functional to unit, performance and accessibility testing
• Have line managed a small team
• Worked with B2C products at scale (millions of users)
• Experience shaping CI/CD pipelines for test reliability (GitHub Actions or similar), including flakiness reduction, parallelisation, artefact management, and runtime control
• Have shipped AI tooling into a quality process — this can be as simple as evaluating, buying and rolling out an off-the-shelf AI testing tool, through to building bespoke agents. You can describe the impact it had on how teams worked
• Proactive, low-ego, and clear communicator, able to chase things down when blocked

Desirable

• Quality at Lendable is a team-level responsibility with participation from PMs, EMs and engineers to ensure the right guardrails are in place to deliver quality software
• We aggressively automate across the whole test pyramid and only manually test where absolutely necessary
• We’re hiring a Head of Quality to be accountable for engineering quality across the organisation, while local responsibility for delivering quality stays with each team
• As we grow in headcount and customer numbers, this role keeps the bar high by taking signals from our existing quality processes, evolving our quality gates, and enabling teams rather than gatekeeping them
• It is a hands-on role with a managerial aspect
• The role will take the lead on using AI in the quality process
• Innovating with LLMs to improve efficiency and accuracy of testing
• What good looks like: in your first 12 months:
• Reduction in incident rate per PR
• SLO coverage across services meaningfully increases
• Introduction of effective AI-assisted quality tooling in the SDLC
• Effective risk based quality reporting on quality
• Own the engineering quality of the organisation
• Service Level Objectives
• Monitor and report on SLO performance
• Engage teams to mature their SLO definitions
• Introduce systematic performance testing in the SDLC
• Ensure initiatives to improve SLO performance are completed
• Own the follow-ups and resolutions from the incident process across engineering
• Ensure follow ups from incidents are completed
• From these and other sources Identify hotspots, catalogue and evaluate risks and recommend remediations
• This is not a pure managerial role
• You will be engaging with teams, innovating with testing solutions and contributing code to test frameworks and CI pipelines.
• Hiring and managing a team of ~2 Quality Engineers, they will be new hires to the company
• Deploying Quality Engineers into engineering teams to help them improve their quality
• Managing and supporting individuals that are embedded into different teams across the organisation
• Evangelising quality across the organisation
• Work with MDs and other senior stakeholders to identify and create solutions to their quality challenges
• Embedding quality thinking into all phases of the SDLC, not just the testing part
• Looking for opportunities to shift-left the testing practices
• Forming a guild of Quality representatives across the organisation
• Scoping and inserting quality initiatives onto teams roadmaps
• Championing these initiatives with senior stakeholders
• Developing the use of AI into the quality process
• Bring tools and processes into the organisation to level-up our testing efficiency using AI. AI testing agents, AI test failure triage agents and AI observers for production environments
• Tech stacks you will be working with:
• Backend Coding: PHP, Kotlin, GO, Python
• Frontend Coding: Typescript, React, React Native, GraphQL, Relay
• Testing: PHPUnit, Behat, JUnit, Kotest, Jest, Maestro, K6
• Observability: Datadog, Sentry, Incident.io, Grafana
• Infrastructure: AWS, Docker, Kubernetes, RabbitMQ
• Deployment: Argo, Github, Github Actions

• Screening Call with the Talent Team
• Hiring Manager Call
• VP of Engineering Call
• Technical Interview
• Final stage with our CTO