Data Quality Engineer

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