At a Glance
- Tasks: Develop and optimise scalable data pipelines using modern tools and technologies.
- Company: Creditsafe empowers organisations with accessible business information, driven by innovation and AI insights.
- Benefits: Enjoy a hybrid work model, hands-on experience with cutting-edge tools, and a supportive culture.
- Why this job: Join a tech-first team transforming data management while fostering curiosity and collaboration.
- Qualifications: 3-5 years in data engineering, proficiency in Python and SQL, and interest in Generative AI.
- Other info: We offer adjustments during recruitment to support your needs—just let us know!
The predicted salary is between 28800 - 48000 £ per year.
Join our UK-based team as a Data Engineer and play a central role in shaping Creditsafe’s modern, AWS-native data platform. This hybrid role requires working from one of our UK offices at least 50% of the time.
About Creditsafe
Privately owned and independently minded, Creditsafe empowers organisations worldwide to make better business decisions. Since our start in Oslo in 1997, we’ve worked to make business information accessible to companies of all sizes—driven by innovation, connected data, and AI-powered insights.
Today, our services help turn complex data into actionable intelligence for risk management, growth, and long-term resilience. We are proud to foster a culture where people can be themselves, thrive professionally, and feel part of a global community.
The Team
Our Data Engineering team is at the forefront of Creditsafe’s transformation into a technology-first, product-led organisation. We’re reimagining how data is collected, modelled, and delivered—investing in cloud scalability, GenAI tooling, and next-generation workflows that empower our engineers to move faster, smarter, and with greater autonomy.
Your Role
As a Data Engineer, you’ll help develop and optimise scalable batch and streaming pipelines, using modern tools and technologies. You’ll work across engineering, analytics, and AI functions to ensure data is accessible, reliable, and actionable.
️ Key Responsibilities
Build, maintain, and optimise batch and streaming pipelines using AWS Glue, Athena, Redshift, and S3.
Use prompt engineering techniques (training provided) to support LLM-based automation and testing.
Collaborate with platform teams to embed GenAI tools (e.g., Cursor, Gemini, Claude) into development workflows.
Curate and manage datasets across structured and unstructured formats and diverse domains.
Contribute to metadata enrichment, lineage, and discoverability using DBT, Airflow, and internal tooling.
Skills & Experience
We value both traditional and non-traditional career paths. You’ll ideally bring:
Technical Skills
3–5 years of experience in data or analytics engineering.
Proficiency in Python and SQL, with strong debugging and performance tuning skills.
Experience building pipelines with AWS services such as Glue, S3, Athena, Redshift, and Lambda.
Familiarity with orchestration tools (e.g., Airflow, Step Functions) and DevOps practices (e.g., CI/CD, Infrastructure as Code).
Interest in Generative AI and a willingness to grow your skills in LLM integration and prompt engineering. (We’ll support your learning through mentoring, internal training, and project exposure.)
Collaboration & Communication
Ability to share knowledge and work effectively across diverse teams.
Comfort working in cross-functional environments with a growth mindset.
Preferred (but not required)
Exposure to GenAI tools (e.g., LangChain, LlamaIndex, or open-source LLMs).
Experience with data cataloguing tools (e.g., OpenMetadata, DataHub) and Data Vault methodology.
Interest in intelligent developer environments (e.g., Cursor, GitHub Copilot, Gemini Code Assist).
Commitment to creating reusable, high-quality data assets for both people and systems.
Accessibility & Adjustments
We are happy to make adjustments at any stage of the recruitment process to support your needs—please let us know what works best for you. This could include:
Extra time or flexibility during interviews
Screen reader-accessible formats
Written rather than verbal communication
Remote options for assessments
Why Join Creditsafe?
Be part of a global company investing in modern, AI-driven data architecture.
Gain hands-on experience with cutting-edge tools—from metadata-aware pipelines to LLM-powered assistants.
Develop in a supportive culture where experimentation and continuous learning are encouraged.
Work in an environment where curiosity, innovation, and collaboration are truly valued.
Ready to Make the Leap into GenAI?
Apply now and bring automation, intelligence, and creativity into every step of the data lifecycle—whether it’s your first GenAI project or your fiftieth.
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Data Engineer ( GenAI & Cloud) employer: Creditsafe
Contact Detail:
Creditsafe Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer ( GenAI & Cloud)
✨Tip Number 1
Familiarise yourself with AWS services like Glue, S3, and Redshift. Understanding these tools will not only help you in interviews but also demonstrate your readiness to hit the ground running in this role.
✨Tip Number 2
Brush up on your Python and SQL skills, especially debugging and performance tuning. Being able to showcase your technical prowess in these areas can set you apart from other candidates.
✨Tip Number 3
Engage with the Generative AI community online. Whether through forums or social media, showing your interest and involvement in GenAI tools will highlight your passion for the field and your willingness to learn.
✨Tip Number 4
Prepare to discuss your experience with data pipelines and orchestration tools like Airflow. Be ready to share specific examples of how you've built or optimised these systems in past roles.
We think you need these skills to ace Data Engineer ( GenAI & Cloud)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data engineering, particularly with AWS services like Glue, S3, and Redshift. Use specific examples to demonstrate your proficiency in Python and SQL.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention your interest in Generative AI and how you plan to grow your skills in LLM integration and prompt engineering.
Showcase Your Technical Skills: Include a section in your application that outlines your technical skills, especially those related to building pipelines and using orchestration tools like Airflow. Be specific about your experience and any projects you've worked on.
Highlight Collaboration Experience: Creditsafe values collaboration, so be sure to mention any experience you have working in cross-functional teams. Provide examples of how you've shared knowledge and contributed to team success.
How to prepare for a job interview at Creditsafe
✨Showcase Your Technical Skills
Be prepared to discuss your experience with AWS services like Glue, S3, and Redshift. Highlight specific projects where you've built or optimised data pipelines, as this will demonstrate your hands-on expertise.
✨Emphasise Collaboration
Creditsafe values teamwork, so be ready to share examples of how you've worked effectively in cross-functional teams. Discuss any experiences where you collaborated with analytics or AI functions to achieve a common goal.
✨Express Your Interest in Generative AI
Since the role involves GenAI tools, show your enthusiasm for this area. Talk about any relevant projects or learning experiences you've had with LLM integration or prompt engineering, even if they are self-initiated.
✨Prepare Questions About Company Culture
Creditsafe prides itself on a supportive culture that encourages experimentation and continuous learning. Prepare thoughtful questions about their approach to professional development and how they foster innovation within the team.