AI Data Engineer

Key Responsibilities:

Design, build and operate scalable data ingestion pipelines across a range of sources, including web data collection, API integrations and batch file deliveries, on our cloud data platform

Build and productionise AI pipelines that use large language models (LLMs) and machine learning to extract, classify, enrich and standardise structured and unstructured data

Develop agentic and retrieval-augmented generation (RAG) workflows, including prompt design, tool and Model Context Protocol (MCP) integrations and orchestration, where they deliver measurable gains in coverage, speed or quality

Establish evaluation and monitoring for AI components, including accuracy benchmarks, human-in-the-loop review, drift detection, and cost and latency tracking

Embed data quality, data governance and Responsible AI practices into every pipeline, in line with Dun & Bradstreet's AI Ethics and data management policies

Research and apply modern data mastering and entity resolution techniques, including AI-assisted matching, to increase derived insight from disparate data sources

Own the operational health of ingestion and AI pipelines in production, including monitoring, incident resolution and continuous optimisation of performance and cost

Contribute to and help lead the AI & Ingestion community of practice by sharing reusable components, patterns and standards, running knowledge-sharing sessions and supporting the upskilling of engineers across teams

Support other engineers through design reviews, code reviews and mentoring

Develop and maintain documentation, including data flow diagrams, data lineage, pipeline runbooks, and prompt and model documentation

Partner with product, data science and business stakeholders to translate data requirements into effective ingestion and AI solutions

Key Requirements:

5+ years of experience in data engineering or a similar position, including hands-on experience building data ingestion pipelines

Strong Python and SQL are minimum requirements

Practical experience building pipelines or applications with LLMs (for example via Anthropic, OpenAI or Google Vertex AI / Gemini APIs), including prompt engineering, structured outputs and evaluation

Experience with cloud data processing, GCP preferred (e.g. BigQuery, Dataflow, Cloud Composer/Airflow, Vertex AI); Databricks experience is an advantage

Experience in various methods of data ingestion and collection, such as web data collection, APIs, SFTP and other file delivery channels

Familiarity with RAG, embeddings and vector databases, agent frameworks or MCP is desirable

Understanding of engineering best practice for AI pipelines (MLOps/LLMOps), including version control, CI/CD, automated testing and monitoring

Awareness of Responsible AI principles and of data privacy and governance considerations (e.g. GDPR) when applying AI to data

A genuine passion for knowledge sharing, with experience of, or strong interest in, contributing to communities of practice, internal tech talks or mentoring

Show an ownership mindset in everything you do; be a problem solver, be curious and be inspired to take action, be proactive, seek ways to collaborate and connect with people and teams in support of driving success.

Continuous growth mindset: keep learning in a fast-moving AI landscape through social experiences and relationships with stakeholders, experts, colleagues and mentors, and widen your competencies through structured courses and programmes.

Where applicable, fluency in English and languages relevant to the working market

Robust data knowledge preferably around business data sets with proven analytical and quantitative skills

Bachelor’s Degree in Data Science field, Information Systems, Computer Science, Mathematics or similar