Dun & Bradstreet
business decisioning data and analytics
DataQualityAgenticSystemsEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Quality Agentic Systems Engineer at Dun & Bradstreet. Skills: agentic and AI-enabled systems, data quality, large language models (LLMs), AI services, data observability, anomaly detection, SQL, Python. designing and building intelligent, agent-based systems that transform how data quality is measured, monitored, and acted upon across D&B’s global platforms and products. create adaptive, decision-capable systems that can reason over data quality signals, orchestrate actions, and support pro”
What You'll Achieve.
transform how data quality is measured, monitored, and acted upon; support proactive data quality management at scale; improve detection, prioritization, and root‑cause analysis; data quality outcomes
Industry & Context.
analytical, problem‑solving, and communication skills; problem-solving; root cause analysis; decision-making
What They're Looking For.
Must Have
Bachelor’s degree in computer science, engineering, or equivalent experience, Advanced experience in software engineering, data engineering, or applied AI systems, proficiency in SQL and Python within complex data environments, Experience designing complex, distributed, or intelligent systems, Solid understanding of data quality concepts and enterprise data ecosystems, analytical, problem‑solving, and communication skills, Ability to work independently and collaborate effectively across globally distributed teams
Nice to Have
cloud computing technologies (preferably GCP)
What You'll Do.
designing and building intelligent
agent-based systems that transform how data quality is measured
and acted upon across D&B’s global platforms and products
decision-capable systems that can reason over data quality signals
and support proactive data quality management at scale
Design and build agentic and AI-enabled systems to support data quality measurement
Develop intelligent agents capable of: Interpreting data quality signals and metrics
Coordinating workflows across systems
Supporting root cause analysis and decision-making
Partner with Data Quality Insights leadership to translate strategic measurement goals into agent-based solutions
Integrate large language models (LLMs) and AI services responsibly into data quality workflows
Build frameworks that allow agents to interact with: Data quality rules and metrics
and monitoring systems
Human-in-the-loop review and governance processes
Apply data observability and anomaly detection concepts to improve detection
and root‑cause analysis
Ensure agentic systems are observable
and aligned with enterprise risk and compliance expectations
Collaborate with business and technical stakeholders to ensure data quality intent and requirements are accurately represented in agent logic
Utilize PowerBI and/or Looker dashboards and reporting outputs as inputs and feedback mechanisms for agent behavior
Communicate with globally distributed stakeholders using JIRA and Confluence
Develop comprehensive documentation of agent architectures
and data quality outcomes
Generate insights and recommendations based on data quality signals and agent outputs
Establish best practices for agent design
and lifecycle management within Data Quality Insights
Provide technical guidance and mentorship to other engineers within the Data Quality Insights organization
Remain current with industry best practices and emerging technologies related to data quality and intelligent systems
How You'll Work.
Team & Collaboration
Collaborate with business and technical stakeholders; Ability to work independently and collaborate effectively across globally distributed teams; Provide technical guidance and mentorship to other engineers within the Data Quality Insights organization
Communication Scope
analytical, problem‑solving, and communication skills; Communicate with globally distributed stakeholders
Full Job Description
## Description 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. For over 180 years, 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. The Role: Data Quality Agentic Systems Engineer is responsible for designing and building intelligent, agent-based systems that transform how data quality is measured, monitored, and acted upon across D&B’s global platforms and products. This role goes beyond traditional automation to create adaptive, decision-capable systems that can reason over data quality signals, orchestrate actions, and support proactive data quality management at scale. ## Key Responsibilities Design and build agentic and AI-enabled systems to support data quality measurement, monitoring, and remediation. Develop intelligent agents capable of: Interpreting data quality signals and metrics Coordinating workflows across systems Supporting root cause analysis and decision-making Partner with Data Quality Insights leadership to translate strategic measurement goals into agent-based solutions. Integrate large language models (LLMs) and AI services responsibly into data quality workflows. Build frameworks that allow agents to interact with: Data quality rules and metrics Metadata, lineage, and monitoring systems Human-in-the-loop review and governance processes Apply data observability and anomaly detection concepts to improve detection, prioritization, and root‑cause analysis. Ensure agentic systems are observable, auditable, and aligned with enterprise risk and compliance expectations. Collaborate with business and technical stakeholders to ensure data quality intent
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