Data Engineer I (R-19886)

Shape the Future with Dun & Bradstreet At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. Since 1841, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at dnb.com/careers.

Coding & Development Write, review, test, and maintain code, including SQL and Python, to build data tools, automations, and ingestion and transformation workflows that support Research and Managed Services Automate manual processes and develop data tools to improve efficiency, accuracy, quality, and throughput Develop and promote coding standards and contribute to code reviews within the agile team Build and maintain web-scraping solutions, API integrations, and reusable data-processing components Support scalable ETL/ELT pipelines for structured and unstructured data Source Evaluation & AI Enablement Identify prospective sources that can feed AI solutions and Research and Managed Services workflows Define and apply source-evaluation criteria covering relevance, authority, freshness, completeness, coverage, consistency, accessibility, legal or licensing constraints, privacy, security, and technical compatibility Perform source profiling, sample validation, proof-of-concept testing, and comparative assessments before recommending onboarding Document source decisions, metadata, lineage, ownership, limitations, refresh expectations, and approved use cases Implement and support AI-enabled workflows using LangChain or equivalent orchestration frameworks, large language models, embeddings, retrieval-augmented generation, vector databases, and prompt-engineering approaches where applicable Monitor source and AI-workflow performance and recommend remediation, replacement, or additional sources when quality or coverage falls below requirements Operational Tasks Perform day-to-day operational activities supporting Research and Managed Services, including monitoring, exception handling, data maintenance, and issue resolution Perform database administration activities, including performance tuning and implementation of best practices Implement new data-maintenance processes and provide end-to-end process ownership Ensure data integrity by validating, reconciling, and regularly cleaning data Investigate and resolve production incidents, pipeline failures, data-quality issues, and operational exceptions Follow applicable data governance, security, and operational standards Collaboration & Continuous Learning Evaluate and implement new technology solutions, and proactively learn and adopt new tools, platforms, and methodologies introduced by the organization Communicate with stakeholders and conduct knowledge-exchange sessions for technical and non-technical audiences Develop and maintain data documentation, including data dictionaries, source assessments, data-flow diagrams, data mappings, runbooks, and data lineage Collaborate with cross-functional teams across Data & Analytics, Technology, Research Services, Managed Services, Product, and Data Governance Additional duties as assigned.

Strong SQL and Python skills, with demonstrated ability to write and maintain code as a core part of daily work Experience with Playwright, Selenium, and other web-data collection techniques Experience developing and supporting data-ingestion, transformation, and ETL/ELT workflows Ability to collect and interpret data from multiple sources, including web scraping and GCS/S3, and formats including delimited files, XML, JSON, and PDF Working knowledge of data systems and databases used to maintain data pipelines Experience with Power BI, Tableau, or other dashboard tools Experience managing stakeholders and project plans Proficiency in Microsoft Office Suite Willingness and demonstrated ability to learn new technologies as they are introduced BigQuery experience and knowledge of AWS and/or GCP Hands-on experience implementing AI solutions using LangChain or an equivalent orchestration framework Exposure large language models, prompt engineering, retrieval-augmented generation, embeddings, vector databases, AI agents, or graph databases Knowledge of Data Operations methodologies, data management approaches, ServiceNow, and/or Jira Experience with NoSQL technologies, SQL Server administration, R programming, web technologies, and data mapping from multiple sources.

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