À propos de ce poste AI Data Engineer (R-20104) chez Dnb
Why We Work at Dun & Bradstreet
We are at a transformational moment in our company journey - and we’re so excited about it. Each day, we are finding new ways to strengthen our award-winning culture, and to accelerate creativity, innovation and growth. Our purpose is to help customers improve business performance with Dun & Bradstreet’s Data Cloud and Live Business Identity, and we’re wildly passionate and committed to this purpose. So, if you’re looking to make an immediate impact at a company that welcomes bold and diverse thinking, come join us!
As a Data Engineer II in our AI & Ingestion team, you will work as part of an agile team building and operating the pipelines that bring new data into Dun & Bradstreet, increasingly using AI to collect, extract, enrich and validate it. You will be responsible for designing, building and running production AI pipelines, from source acquisition and ingestion through to AI-powered extraction, classification and entity matching, and delivery against our data quality standards.
Your focus will be on applying modern cloud-native and generative AI services across the ingestion lifecycle, harnessing their scale and automation to accelerate how we onboard and curate data, while keeping accuracy, integrity, completeness, conformity and consistency at the core. Beyond delivery, you will play an active role in our AI & Ingestion community of practice, sharing patterns, tooling and lessons learned so that good practice is adopted across teams. Continuous improvement will be a key measure of success in this role.
Key Responsibilities:
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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
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Build and productionise AI pipelines that use large language models (LLMs) and machine learning to extract, classify, enrich and standardise structured and unstructured data
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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
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Establish evaluation and monitoring for AI components, including accuracy benchmarks, human-in-the-loop review, drift detection, and cost and latency tracking
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Embed data quality, data governance and Responsible AI practices into every pipeline, in line with Dun & Bradstreet's AI Ethics and data management policies
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Research and apply modern data mastering and entity resolution techniques, including AI-assisted matching, to increase derived insight from disparate data sources
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Own the operational health of ingestion and AI pipelines in production, including monitoring, incident resolution and continuous optimisation of performance and cost
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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
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Support other engineers through design reviews, code reviews and mentoring
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Develop and maintain documentation, including data flow diagrams, data lineage, pipeline runbooks, and prompt and model documentation
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Partner with product, data science and business stakeholders to translate data requirements into effective ingestion and AI solutions
Key Requirements:
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5+ years of experience in data engineering or a similar position, including hands-on experience building data ingestion pipelines
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Strong Python and SQL are minimum requirements
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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
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Experience with cloud data processing, GCP preferred (e.g. BigQuery, Dataflow, Cloud Composer/Airflow, Vertex AI); Databricks experience is an advantage
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Experience in various methods of data ingestion and collection, such as web data collection, APIs, SFTP and other file delivery channels
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Familiarity with RAG, embeddings and vector databases, agent frameworks or MCP is desirable
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Understanding of engineering best practice for AI pipelines (MLOps/LLMOps), including version control, CI/CD, automated testing and monitoring
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Awareness of Responsible AI principles and of data privacy and governance considerations (e.g. GDPR) when applying AI to data
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A genuine passion for knowledge sharing, with experience of, or strong interest in, contributing to communities of practice, internal tech talks or mentoring
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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.
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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.
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Where applicable, fluency in English and languages relevant to the working market
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Robust data knowledge preferably around business data sets with proven analytical and quantitative skills
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Bachelor’s Degree in Data Science field, Information Systems, Computer Science, Mathematics or similar
