Senior QA Automation Engineer

Kingsland, Greater London
7 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Governance Analyst

Senior Data Engineer

Senior Data Engineer (AWS, Airflow, Python)

Senior Data Engineer

Senior Data engineer - Databricks

Ready to take the lead in back-end QA engineering?

This is your opportunity to step into a Senior QA Automation Engineer role where your passion for API testing, performance validation, and backend automation is truly valued.

Join a collaborative, fast-moving team that’s shaping the future of retail tech—partnering with high-profile global brands to deliver seamless customer experiences at scale.

Their platform powers clienteling, POS, and customer success—all in one. And it’s their clients’ real-world use cases that drive the roadmap, ensuring the tech evolves to meet real needs.

Role: Senior QA Automation Engineer (Backend/API Focus)
Salary: Up to £75,000
Benefits: 25 days holiday + BH, Company Bonus, Private Healthcare, Share Options, every other Friday off (compressed working)
Location: Remote-first – occasional meetups in London HQ / 9-day fortnight

What You’ll Bring

Deep experience in API and backend test automation
Proficiency in Python or JavaScript, with strong scripting and automation capability
Hands-on with tools like Postman, PyTest, Locust, K6, or similar for load, performance & functional testing
Solid understanding of CI/CD pipelines using GitHub Actions, Azure DevOps, etc.
Comfortable working with NoSQL databases and validating backend data integrity
Skilled in test strategy, debugging, and collaborating closely with backend developers
Bonus: exposure to mobile or front-end testing with Appium or Selenium DevOps, GitHub Actions
Familiarity with NoSQL databases
Excellent problem-solving and communication skills  
We are an equal opportunity recruitment company. This means we welcome applications from all suitably qualified people regardless of race, sex, disability, religion, sexual orientation or age.

We are particularly invested in Neurodiversity inclusion and offer reasonable adjustments in the interview process. Reasonable adjustments are changes that we can make in the interview process if your disability puts you at a disadvantage compared with others who are not disabled. If you would benefit from a reasonable adjustment in your interview process, please call or email one of our recruiters

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.