Test Specialist

Leeds
2 months ago
Applications closed

Related Jobs

View all jobs

Lead data Engineer - Financial Markets - Day rate

ADAS Engineer

Senior ADAS Engineer

Paid Search Lead

Senior Data Scientist London, England

Data Engineer

We're looking for a highly experienced Test Specialist to join our Data Engineering and Analytics function. Our Test Specialist will play a crucial role in modernising and optimising test processes across the function, with a specific focus on test automation in the context of data engineering and analytics delivery. This role involves working closely with our Test Manager, and across multiple agile delivery teams to ensure the highest quality of data ingestion, transformation pipelines (ETL/ELT), and analytical models.

As our Test Specialist, you'll have access to a wide range of benefits including:

Access to a generous discretionary profit share scheme
Colleague discounts on Jet2.com and Jet2holidays flights
Hybrid working
What you'll be doing:

Test Process Modernisation and Optimisation:

Working alongside our Test Manager, lead the modernisation and optimisation of test processes across the data engineering and analytics function.
Identify gaps and opportunities in existing testing practices and drive initiatives to address them.
Develop and implement test automation strategies to enhance efficiency and effectiveness.
Test Strategy and Automated Framework Development for various testing phases like system testing, integration testing, non-functional testing (performance, security)
Technical Testing:

Support teams and key initiatives where required by conducting testing across data ingestion and transformation pipelines (ETL/ELT)
Validate analytical data models to ensure accuracy and reliability.
Utilize cloud platforms (AWS, GCP) and tools such as Snowflake, dBt, Fivetran, and Tableau for testing purposes.
Thought Leadership and Collaboration:

Act as a thought leader for testing within the data analytics space advocating for modern testing methodologies and technologies like Test Driven development and behaviour driven development or AI ML Based testing approaches for ensuring data quality, anomaly detection and performance.
Collaborate with existing testing teams to align on best practices and innovative testing approaches.
Process Improvement and Upskilling:

Drive continuous improvement in test practices, processes, and automation.
Mentor and train existing test team members to adopt new testing methodologies and tools with a major focus on enabling the wider test and engineering teams to make more use of test automation and other advanced testing techniques over manual testing capability.
What you'll have:

Technical Skills:

Extensive experience in a technical testing role within a data analytics or related function.
Proficiency in testing data ingestion and transformation pipelines (ETL/ELT)
Hands-on experience with cloud platforms (AWS, GCP) and tools such as Snowflake, dBt, Fivetran, and Tableau (or similar)
Strong knowledge of SQL and experience in testing databases and data warehouses with dBt (e.g., Snowflake - Preferred, Redshift, BigQuery)
Strong Knowledge of workload automation platforms like Apache Airflow and dBt (Data Build Tool)
Familiarity with CI/CD tools (e.g. Azure DevOps - Preferred, Jenkins) and experience integrating automated tests into pipelines.
Experience with cloud platforms (AWS - Preferred, GCP, Azure) for testing and deploying data solutions.
Proficiency in any programming languages (Python - Prferred, Java, Scala, or similar) for developing test automation scripts and frameworks.
Proficiency with automation testing frameworks (Cucumber, Gherkin, TestNG, or similar) for data testing workloads.
Knowledge of performance testing and load testing tools (Apache JMeter or Gatling)
Experience:

Proven track record in supporting and improving test processes in data-related projects.
Experience in leadership, mentoring, or training roles is highly advantageous.
Desirable Qualifications:

Certifications in relevant cloud platforms (AWS, GCP)
Experience with additional data integration and analytics tools.
Knowledge of best practices in data governance and security.
ISTQB Fundamentals or Advanced Certification.
Join us as we redefine travel experiences and create memories for millions of passengers. At Jet2.com and Jet2holidays, your potential has no limits. Apply today and let your career take flight!

#LI-Hybrid

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.

Jobs

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Jobs Careers

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Jobs

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.