À propos de ce poste Software Engineer II (R-20067) chez Dnb
B3 Quality Assurance Engineer – MDM Technology
Our MDM team is seeking an experienced Quality Assurance Engineer, to join an established engineering team. You will work on our D&B Connect application, a strategic self-service Master Data Management (MDM) platform that enables customers to cleanse, match, enrich, monitor, and manage their first-party data using D&B's Commercial Graph and D-U-N-S®-anchored data assets. This serves as our primary MDM customer-facing platform, accessed via web UI plus CRM, Marketing and Cloud Data Warehouse integrations with future support for AI headless access via MCP tools.
The QA engineer will create and maintain tests to ensure software meets user and product needs. This includes developing test plans, tracking quality assurance metrics, documenting defects and supporting issue resolution and continuous process improvement.
The successful candidate should be data curious and have a track record of using AI tools to bring greater efficiency to the software testing lifecycle.
Team Overview
- Design and evolve enterprise-grade Master Data Management (MDM) platforms powering core business entities (e.g., customers, suppliers, leads, reference data)
- Solve challenges in data quality, identity resolution, integration, and governance at scale
- Build highly available, resilient systems that support both real-time and batch workloads across global regions
- Collaborate with product, data, and platform teams to transform business needs into scalable technical solutions
Key Responsibilities:
- Partner with development and product teams to ensure product quality in a fast-paced Agile environment
- Create and maintain test strategies, test plans, test cases, test data and traceability for new features and defect fixes
- Write and maintain automated tests for data-validation workflows
- Integrate and maintain automated test suites within CI/CD pipelines to support continuous testing, quality gates and release validation throughout the software delivery lifecycle
- Perform manual functional, regression, integration, and exploratory testing when automation is not suitable
- Validate multi-tiered, data-driven applications processing large volumes of records
- Define and track quality metrics including defect counts, severity, test execution, coverage and release status
- Identify, document, report, track and verify issues through to resolution
- Perform root-cause analysis and collaborate on preventive quality improvements
- Support product deployments, release validation and QA sign-off
- Partner with support teams to reproduce and troubleshoot customer-reported issues
- Proactively improve quality through collaboration, automation, process improvement and innovative thinking
- Stay current with emerging AI technologies, tools, and best practices