Data Engineer ( GenAI & Cloud)

Data Engineer – Hybrid, UK (50% in-office)
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 , 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.