To strengthen Agilent’s data management foundation by ensuring that critical commercial data assets are clearly defined, cataloged, governed, and aligned to enterprise data standards. By advancing metadata management, data catalog adoption, and sustainable data quality practices, the position will improve commercial readiness and create the trusted data foundation needed for scalable analytics and AI-driven use cases. 

We are seeking an experienced Data Governance & Quality Engineer to play a key role in shaping Agilent's modern data management landscape. 

In this role, you will combine data governance and data quality expertise to ensure business-critical data assets are governed, discoverable, trusted, and ready to support operational, analytical, and AI-driven use cases. You will help establish and maintain governance processes for data assets within Microsoft Fabric and enterprise data catalog environments, while partnering with business stakeholders, data owners, architects, and analytics teams to improve the quality, usability, and transparency of commercial data. 

This role is ideal for a professional who enjoys working at the intersection of business processes, policy, technology, and data governance and who wants to help build the foundation for the next generation of analytics and AI capabilities. 

Key Responsibilities 

Data Governance & Metadata Management 

• Become a subject matter expert in Customer Master Data and partner with business teams to establish governance standards, accountability models, and processes, governed data products, and enterprise reporting assets  

• Govern critical data assets by defining and maintaining metadata, business context definitions, ownership, stewardship responsibilities, classifications, quality requirements, and certification criteria. 

• Manage enterprise data catalog content, business glossary entries, lineage documentation, and impact analysis to keep data assets discoverable, understandable, and trusted. 

Modern Data Platforms & Analytics 

• Support Agilent's adoption of Microsoft Fabric, Power BI, Snowflake, and modern data management capabilities, with a focus on governed, reusable, and business-ready data assets.

• Use SQL,Data Catalogs,Power BI, and related tools to analyze data,validategovernance controls, and support quality and metadata monitoring activities.

• Collaborate with Data Architecture, Data Engineering, and Analytics teams to embed governance, catalog, semantic layer, and monitoring requirements into solution design and delivery.

Data Quality Engineering

• Profile enterprise data to assess quality,identifyrisks, establishbaselines, and define data quality rules, KPIs, and monitoring processes for critical business data.

• Perform root cause analysis and drive sustainable remediation of data quality issues in partnership with business and technical teams.

• Develop monitoring and scorecard solutions that provide visibility into data quality, governance performance, and commercial readiness.

Business Partnership

• Act as a trusted advisor for system implementations, integrations, business process changes, and digital transformation initiatives where data governance and quality are critical to commercial readiness.

• Partner with stakeholders across Sales, Services, Marketing, and Operations to promote data literacy, stewardship, and practical governance best practices.