About The Role

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.

Key Responsibilities

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.

Key Skills

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.