AI Data Engineer

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