Description

Key Responsibilities

• Define, maintain, and evolve the enterprise data quality rubric across all data domains
• Establish standards for data accuracy, completeness, consistency, timeliness, and reliability
• Ensure data quality expectations are clearly defined and consistently applied across the platform
• Govern how data quality is measured, scored, and reported

• Design and implement automated data quality checks within data pipelines
• Build validation rules that detect anomalies, schema drift, missing data, and inconsistencies
• Ensure issues are identified at the source before propagating downstream
• Continuously improve validation coverage and effectiveness

• Build and maintain observability systems for pipeline health, data freshness, and performance
• Monitor data flows for failures, delays, and unexpected changes
• Provide visibility into pipeline status and data quality metrics across the platform
• Implement alerting and reporting mechanisms for critical issues

• Diagnose data quality issues and trace them back to source systems or pipeline logic
• Partner with Data Engineers to resolve issues at the pipeline level
• Work with product and AI teams to understand how data issues impact tool behavior
• Ensure root causes are addressed and not repeated

• Work with the Lead Data Engineer to align on pipeline architecture and quality standards
• Partner with pod Data Engineers to embed quality checks into all pipelines
• Collaborate with Lead Engineers and Applied AI Engineers to understand downstream impacts
• Communicate data quality insights clearly to both technical teams and leadership

• Score and report on data quality across the platform on a defined cadence
• Provide leadership with a clear view of data health, risks, and improvement areas
• Identify systemic issues and drive improvements in data processes and standards
• Continuously refine data quality practices as the platform evolves

Skills, Knowledge and Expertise

• Experience designing scalable technical architectures for AI or machine learning solutions in enterprise environments
• Strong understanding of large language models, vector databases, embeddings, prompt orchestration, and model serving
• Hands-on experience with Azure services including Azure OpenAI, Azure Machine Learning, and Azure Functions
• Familiarity with LLM frameworks and orchestration tools such as LangChain, Semantic Kernel, or custom agent frameworks
• Knowledge of enterprise security, responsible AI principles, and compliance frameworks such as GDPR and CCPA
• Proven ability to create architecture documentation and communicate effectively with technical and non-technical audiences
• Experience integrating AI solutions into platforms such as Power Platform, SharePoint, and Microsoft Teams
• Bachelor’s or master’s degree in computer science, data science, engineering, or related field
• Certifications in cloud architecture or AI/ML disciplines preferred

About Trilon

About Trilon Group

Our hiring process