RELX Data Engineer interview questions & guide 2026

The following questions reflect the core competencies and technical focus areas identified for Data Engineer roles at RELX. These are representative of the patterns you will encounter during your assessment.

Technical & Cloud Infrastructure

These questions evaluate your proficiency with cloud-native tools, pipeline architecture, and your ability to manage large-scale data systems.

• Explain your experience with designing ETL/ELT pipelines in a cloud environment.
• How do you ensure data quality and integrity when scaling pipelines?

Preparation for RELX should be systematic. You should focus on demonstrating both your technical depth and your ability to work within a collaborative, product-focused environment.

Role-Related Knowledge – You must possess a strong grasp of cloud technologies and data modeling principles. Interviewers will look for your ability to explain the "why" behind your technical choices, not just the "how."

Problem-Solving Ability – RELX values engineers who can deconstruct ambiguous problems. Be prepared to walk through your logic step-by-step when presented with a design scenario, focusing on trade-offs and scalability.

Communication & Collaboration – You will likely interact with cross-functional teams, including product managers and data scientists. Show that you can communicate complex technical concepts to non-technical stakeholders clearly and effectively.

The interview process at RELX is designed to evaluate your technical capability, architectural thinking, and cultural alignment. You can expect a rigorous but structured progression that begins with an initial screening to gauge your background and interest. Following this, you will move into technical deep-dives, which may include coding assessments, system design discussions, and behavioral interviews that explore your past projects and team dynamics.

The pace is professional and methodical. You should be prepared to discuss specific technical challenges you have solved in previous roles, providing concrete examples of your contributions. The hiring team focuses on your ability to work within established engineering standards while contributing to the innovation of their data platforms.

This visual timeline illustrates the typical sequence of events, from initial recruiter screens to final technical and behavioral evaluations. Use this to pace your study schedule, ensuring you are prepared for both the technical coding requirements and the higher-level architectural discussions that occur in the later stages.

Data Pipeline Architecture

This area is critical as it defines your ability to build scalable, reliable systems. You are expected to demonstrate knowledge of modern data tools and best practices for automation.

Be ready to go over:

• ETL/ELT Frameworks – Understanding the strengths and limitations of different orchestration tools.
• Data Warehousing – Proficiency in cloud-based storage and query optimization.

As a Data Engineer, your day-to-day will involve the lifecycle of data assets. You will be responsible for developing and maintaining the infrastructure that supports RELX products. This involves writing clean, efficient code, managing data migrations, and collaborating with architects to ensure the system is future-proof.

You will frequently interface with product teams to gather requirements, translating business needs into technical specifications. You will also be expected to drive initiatives that improve data quality, documentation, and operational efficiency. The work is dynamic and requires a balance of hands-on coding and strategic planning.

A strong candidate for this position should demonstrate a blend of technical mastery and collaborative spirit.

• Must-have skills: Proficiency in SQL, Python, and experience with at least one major cloud provider (e.g., AWS, Azure, or GCP). You should have a proven track record of managing end-to-end data pipelines.
• Nice-to-have skills: Experience with containerization (Docker/Kubernetes), CI/CD pipelines, and familiarity with AI/ML infrastructure.
• Soft skills: Excellent documentation habits, a proactive approach to problem-solving, and the ability to thrive in a global, matrixed organization.

Q: How long does the interview process typically take? The timeline varies, but candidates generally complete the cycle within a few weeks. Consistency and clear communication with your recruiter will help keep the process moving efficiently.

Q: What is the most important thing to emphasize during the interview? Focus on the impact of your work. Don't just list technologies; explain how your engineering decisions improved reliability, reduced costs, or enabled new features for users.

Q: Does RELX prioritize specific technical certifications? While certifications are a nice addition, they are secondary to proven, hands-on experience. Focus on your ability to articulate how you have used your skills to solve real-world problems.

• Review your resume: Be prepared to dive deep into every project you list. You will be asked about the trade-offs you made and what you would do differently today.
• Understand the business: Research the products RELX offers. Knowing the "why" behind the data you are engineering adds significant value to your answers.
• Practice system design: Sketch out your architectures on a whiteboard or digital tool. Being able to visualize your design is essential for the system design rounds.

The Data Engineer position at RELX offers a unique opportunity to work on high-impact data platforms that shape global professional information. By mastering your technical fundamentals, focusing on system scalability, and demonstrating a clear, collaborative communication style, you will position yourself as a top-tier candidate.

Candidates can explore additional interview insights, practice questions, and preparation resources on Dataford. Dedicating time to refine your responses and understanding the specific architectural challenges typical of RELX will significantly boost your confidence and performance. You have the skills to excel, so approach your interviews with preparation and a clear focus on the value you bring to the team.

The salary data provided represents current market ranges for this role. Use this to understand the compensation expectations for your level and location, keeping in mind that total packages may include additional benefits and performance-based components.