Test Automation Engineer - 12 month FTC

Brighton
2 weeks ago
Create job alert

Test Automation Engineer
Location: Hybrid / Brighton (1-2 days a week in Brighton)
Salary: £55,000 - £65,000 (plus bonus, Excellent Pension and Employee Benefits)
12 month Fixed Term Contract

Skills: Playwright or ReqnRoll / Specflow, Selenium, TDD, SQL Server, C#.Net, Automated Testing

Join a busy and successful Test Team, working alongside Agile Dev Teams to deliver on a range of important projects. Our Client is a high profile Government body that provides essential services to protect peoples savings in the UK. If you are an experienced Automation Tester who can prioritise and a natural problem solver with a 'can-do' approach please get in touch!

Responsibilities

Lead and peer-review the work of other test team members. Lead on investigative work into problems and opportunities in existing processes.
Input into the test strategy team and advise on automation testing with recommendations for improvement
From scratch, develop the product and development teams' approach to testing - e.g. a framework for test driven development
Design and develop automated test scripts with a focus on SQL-based data validation, ensuring the accuracy of data flows and transformation.
Integrate automated tests into CI/CD pipelines.
Be the person on-point and overall responsible for the automation testing delivery within the Platform team.
Demonstrate knowledge of emerging tools and techniques.
Effectively work across different tools and software languages.Essential

5+ years automation and manual testing experience of web-based applications.
Playwright and ReqnRoll / Specflow knowledge and experience
Experienced in C#.Net and SQL Server.
Experience crafting automation tests and demonstrating test frameworks and pipelines, collaborating in any of the following: RESTful services, Message Queues, Relational Databases.
Experience of SQL scripting and proven capability of writing sophisticated queries to analyse problems, and interrogate data pipelines
Experience a range of testing methodologies and techniques including agile, with the ability to write test procedures, plans and report development/implementation

Related Jobs

View all jobs

LabVIEW Automation & Test Engineer

QA Automation Engineer

Senior QA Engineer

Lead Salesforce Developer

Automation & Manual Tester

Software Engineering Manager

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Quantum-Enhanced AI in Data Engineering: Reshaping the Big Data Pipeline

Data engineering has become an indispensable pillar of the modern technology ecosystem. As companies gather massive troves of data—often measured in petabytes—the importance of robust, scalable data pipelines cannot be overstated. From ingestion and storage to transformation and analysis, data engineers stand at the forefront of delivering reliable data for analytics, machine learning, and critical business decisions. Simultaneously, the field of Artificial Intelligence (AI) has undergone a revolution, transitioning from niche research projects to a foundational tool for everything from predictive maintenance and fraud detection to customer experience personalisation. Yet as AI models grow in complexity—think large language models with hundreds of billions of parameters—the data volumes and computational needs escalate dramatically. The industry finds itself at an inflection point: traditional computing systems may eventually hit performance ceilings, even when scaled horizontally with thousands of nodes. Enter quantum computing, a nascent yet rapidly progressing technology that leverages quantum mechanics to tackle certain computational tasks exponentially faster than classical machines. While quantum computing is still maturing, its potential to supercharge AI workflows—often referred to as quantum-enhanced AI—has piqued the curiosity of data engineers and enterprises alike. This synergy could solve some of the biggest headaches in data engineering: accelerating data transformations, enabling more efficient analytics, and even facilitating entirely new kinds of modelling once believed to be intractable. In this article, we explore: How data engineering has evolved to support AI’s insatiable appetite for high-quality, well-structured data. The fundamentals of quantum computing and why it may transform the data engineering landscape. Potential real-world applications for quantum-enhanced AI in data engineering—from data ingestion to machine learning pipeline optimisation. Emerging career paths and skill sets needed to thrive in a future where data, AI, and quantum computing intersect. Challenges, ethical considerations, and forward-looking perspectives on how this convergence might shape the data engineering domain. If you work in data engineering, are curious about quantum computing, or simply want to stay on the cutting edge of technology, read on. The next frontier of data-driven innovation may well be quantum-powered.

Data Engineering Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Data. It’s the critical lifeblood of every forward-thinking organisation, fueling everything from strategic decision-making to real-time analytics. As data volumes skyrocket and technologies mature, the UK has distinguished itself as a frontrunner in data innovation. A robust venture capital scene, government-backed initiatives, and a wealth of academic talent have created fertile ground for data-centric start-ups across the country. In this Q3 2025 Investment Tracker, we’ll delve into the newly funded UK start-ups shaping the future of data engineering. More importantly, we’ll explore the rich job opportunities that have emerged alongside these funding announcements. From building scalable ETL (Extract, Transform, Load) pipelines to architecting data warehouses and implementing advanced data governance frameworks, data engineers, architects, and analysts have an incredible array of roles to pursue. If you’re eager to elevate your career in data engineering, read on for insights into the most dynamic start-ups, their fresh capital injections, and the skill sets they’re hungry for.

Portfolio Projects That Get You Hired for Data Engineering Jobs (With Real GitHub Examples)

Data is increasingly the lifeblood of businesses, driving everything from product development to customer experience. At the centre of this revolution are data engineers—professionals responsible for building robust data pipelines, architecting scalable storage solutions, and preparing data for analytics and machine learning. If you’re looking to land a role in this exciting and high-demand field, a strong CV is only part of the puzzle. You also need a compelling data engineering portfolio that shows you can roll up your sleeves and deliver real-world results. In this guide, we’ll cover: Why a data engineering portfolio is crucial for standing out in the job market. Choosing the right projects for your target data engineering roles. Real GitHub examples that demonstrate best practices in data pipeline creation, cloud deployments, and more. Actionable project ideas you can start right now, from building ETL pipelines to implementing real-time streaming solutions. Best practices for structuring your GitHub repositories and showcasing your work effectively. By the end, you’ll know exactly how to build and present a portfolio that resonates with hiring managers—and when you’re ready to take the next step, don’t forget to upload your CV on DataEngineeringJobs.co.uk. Our platform connects top data engineering talent with companies that need your skills, ensuring your portfolio gets the attention it deserves.